Supporting Information: ‘Sex differences in allometry for phenotypic traits indicate that females are not scaled males’
Laura A. B. Wilson, Susanne R. K. Zajitschek, Malgorzata Lagisz, Jeremy Mason, Hamed Haselimashhadi & Shinichi Nakagawa
The document mainly provide the description of the main dataset, and the R scripts and their outputs for the paper “Sex differences in allometry for phenotypic traits indicate that females are not scaled males”.
Setting-up
Loading packages
# older version of the orchaRd package
#devtools::install_github("itchyshin/orchard_plot", subdir = "orchaRd", force = TRUE, build_vignettes = TRUE)
pacman::p_load(tidyverse,
purrr,
metafor,
poolr,
patchwork,
orchaRd, # older version:
broom.mixed,
here,
nlme,
pander,
brms,
kableExtra,
formatR,
knitr,
brms,
rstan
)Loading custum functions
We load custom function not included in the packages above.
- functions for centering each group to its mean (group-wise
centering):
groupScale - functions for calculating ‘parameters’ (intercepts, slopes and
residuals SDs for both males and females) :
get_parmetersN - functions for drawing orchard plots (modified from the original):
orchard_plot2and assoicated functions.
# custom function for within-group cenering (or z transformation)
groupScale <- function(formula, data=NULL, center=TRUE, scale=FALSE){
if(is.null(data)) data <- model.frame(formula)
scaled <- rep(NA,nrow(data)) #empty vector
for(i in unique(data[,2])){
elements <- which(data[,2]==i)
scaled[elements] <- scale(data[elements,1], scale=scale, center=center)
}
return(scaled)
}
# function to get what we need from these 2 models (you can include models in this function as well)
get_parmetersN<- function(i){
# centering weights separately for each
ln_c_weight <- groupScale(log(i[["weight"]]) ~ i[["sex"]])
i[,"ln_c_weight"] <- ln_c_weight
if(i[["nmeta"]][1] == 1 && i[["nstrain"]][1] == 1){
# female model
model_f <- lme(log(data_point2) ~ sex*ln_c_weight,
random = list(#metadata_group = ~ ln_c_weight,
#strain_name = ~ ln_c_weight,
date_of_experiment = ~ 1),
weights = varIdent(form = ~1 | sex),
control = lmeControl(opt = "optim"),
data = i)
# male model
model_m <- lme(log(data_point2) ~ relevel(sex, ref = "male")*ln_c_weight,
random = list(#metadata_group = ~ ln_c_weight,
#strain_name = ~ ln_c_weight,
date_of_experiment = ~ 1),
weights = varIdent(form = ~1 | sex),
control = lmeControl(opt = "optim"),
data = i)
# neutral model
model_n <- lme(log(data_point2) ~ sex*ln_c_weight,
random = list(#metadata_group = ~ ln_c_weight,
#strain_name = ~ ln_c_weight,
date_of_experiment = ~ 1),
#weights = varIdent(form = ~1 | sex),
control = lmeControl(opt = "optim"),
data = i)
} else if (i[["nmeta"]][1] == 1) {
# female model
model_f <- lme(log(data_point2) ~ sex*ln_c_weight,
random = list(#metadata_group = ~ ln_c_weight,
strain_name = ~ ln_c_weight,
date_of_experiment = ~ 1),
weights = varIdent(form = ~1 | sex),
control = lmeControl(opt = "optim"),
data = i)
# male model
model_m <- lme(log(data_point2) ~ relevel(sex, ref = "male")*ln_c_weight,
random = list(#metadata_group = ~ ln_c_weight,
strain_name = ~ ln_c_weight,
date_of_experiment = ~ 1),
weights = varIdent(form = ~1 | sex),
control = lmeControl(opt = "optim"),
data = i)
# neutral model
model_n <- lme(log(data_point2) ~ sex*ln_c_weight,
random = list(#metadata_group = ~ ln_c_weight,
strain_name = ~ ln_c_weight,
date_of_experiment = ~ 1),
#weights = varIdent(form = ~1 | sex),
control = lmeControl(opt = "optim"),
data = i)
} else if (i[["nstrain"]][1] == 1){
# female model
model_f <- lme(log(data_point2) ~ sex*ln_c_weight,
random = list(metadata_group = ~ ln_c_weight,
#strain_name = ~ ln_c_weight,
date_of_experiment = ~ 1),
weights = varIdent(form = ~1 | sex),
control = lmeControl(opt = "optim"),
data = i)
# male model
model_m <- lme(log(data_point2) ~ relevel(sex, ref = "male")*ln_c_weight,
random = list(metadata_group = ~ ln_c_weight,
#strain_name = ~ ln_c_weight,
date_of_experiment = ~ 1),
weights = varIdent(form = ~1 | sex),
control = lmeControl(opt = "optim"),
data = i)
# neutral model
model_n <- lme(log(data_point2) ~ sex*ln_c_weight,
random = list(metadata_group = ~ ln_c_weight,
#strain_name = ~ ln_c_weight,
date_of_experiment = ~ 1),
#weights = varIdent(form = ~1 | sex),
control = lmeControl(opt = "optim"),
data = i)
} else {
# female model
model_f <- lme(log(data_point2) ~ sex*ln_c_weight,
random = list(metadata_group = ~ ln_c_weight,
strain_name = ~ ln_c_weight,
date_of_experiment = ~ 1),
weights = varIdent(form = ~1 | sex),
control = lmeControl(opt = "optim"),
data = i)
# male model
model_m <- lme(log(data_point2) ~ relevel(sex, ref = "male")*ln_c_weight,
random = list(metadata_group = ~ ln_c_weight,
strain_name = ~ ln_c_weight,
date_of_experiment = ~ 1),
weights = varIdent(form = ~1 | sex),
control = lmeControl(opt = "optim"),
data = i)
# neutral model
model_n <- lme(log(data_point2) ~ sex*ln_c_weight,
random = list(metadata_group = ~ ln_c_weight,
strain_name = ~ ln_c_weight,
date_of_experiment = ~ 1),
#weights = varIdent(form = ~1 | sex),
control = lmeControl(opt = "optim"),
data = i)
}
# getting all we want
females <- broom.mixed::tidy(model_f)
males <- broom.mixed::tidy(model_m)
# gets variance weights
weights <- attr(model_f$modelStruct$varStruct, "weights")
male_correction <- 1/weights[which(names(weights) == "male")[1]]
female_correction <- 1/weights[which(names(weights) == "female")[1]]
# get parameters
parameter_name <- tolower(i[["parameter_name"]][1])
procedure_name <- i[["procedure_name"]][1]# "procedure_name"
m_n <- sum(i[["sex"]] == "male") # sample size for males
f_n <- sum(i[["sex"]] == "female") # N fo females
f_intercept <- as.numeric(females[1, 4])
f_intercept_se <- as.numeric(females[1, 5])
f_slope <- as.numeric(females[3, 4])
f_slope_se <- as.numeric(females[3, 5])
m_intercept <- as.numeric(males[1, 4])
m_intercept_se <- as.numeric(males[1, 5])
m_slope <- as.numeric(males[3, 4])
m_slope_se <- as.numeric(males[3, 5])
fm_diff_int <- as.numeric(males[2, 4])
fm_diff_int_se <- as.numeric(males[2, 5])
fm_diff_int_p <- as.numeric(males[2, 8])
fm_diff_slope <- as.numeric(males[4, 4])
fm_diff_slope_se <- as.numeric(males[4, 5])
fm_diff_slope_p <- as.numeric(males[4, 8])
# variance component
#group_sd <- as.numeric(VarCorr(model_f)[,2][2])
#g_slope_sd <- as.numeric(VarCorr(model_f)[,2][3])
#batch_sd <- as.numeric(VarCorr(model_f)[,2][5])
f_sd <- as.numeric(tail(VarCorr(model_f)[,2],1))*female_correction
m_sd <- as.numeric(tail(VarCorr(model_f)[,2],1))*male_correction
# model fit
r_m <- sqrt(MuMIn::r.squaredGLMM(model_n)[1,1])
r_c <- sqrt(MuMIn::r.squaredGLMM(model_n)[1,2])
# putting it together
paras <- data.frame(parameter_name, procedure_name,
f_n, m_n, f_intercept, f_intercept_se, f_slope, f_slope_se,
m_intercept, m_intercept_se, m_slope, m_slope_se,
fm_diff_int, fm_diff_int_se, fm_diff_int_p,
fm_diff_slope, fm_diff_slope_se, fm_diff_slope_p,
f_sd, m_sd, r_m, r_c)
names(paras) <- c('parameter_name', 'procedure_name',
'f_n', 'm_n','f_intercept', 'f_intercept_se', 'f_slope', 'f_slope_se',
'm_intercept', 'm_intercept_se', 'm_slope', 'm_slope_se',
'fm_diff_int', 'fm_diff_int_se', 'fm_diff_int_p',
'fm_diff_slope', 'fm_diff_slope_se', 'fm_diff_slope_p',
'f_sd', 'm_sd', 'r_m', 'r_c') # variance component
invisible(paras)
}
# getting ride of traits which do not run
get_para_poss <- possibly(.f = get_parmetersN,
otherwise = NULL)
# functions
orchard_plot2 <- function (object, mod = "Int", xlab, N = "none", alpha = 0.5,
angle = 90, cb = FALSE, k = TRUE, transfm = c("none", "tanh"),
point.size = 2.5, branch.size = 5,
condition.lab = "Condition", legend.on = TRUE)
{
transfm <- match.arg(transfm)
if (any(class(object) %in% c("rma.mv", "rma"))) {
if (mod != "Int") {
object <- mod_results(object, mod)
}
else {
object <- mod_results(object, mod = "Int")
}
}
mod_table <- object$mod_table
data <- object$data
data$moderator <- factor(data$moderator, levels = mod_table$name,
labels = mod_table$name)
data$scale <- (1/sqrt(data[, "vi"]))
legend <- "Precision (1/SE)"
# sample size
if(any(N != "none")){
data$scale <- N
legend <- "Sample size (N)" # we want to use italic
}
if (transfm == "tanh") {
cols <- sapply(mod_table, is.numeric)
mod_table[, cols] <- Zr_to_r(mod_table[, cols])
data$yi <- Zr_to_r(data$yi)
label <- xlab
}
else {
label <- xlab
}
mod_table$K <- as.vector(by(data, data[, "moderator"], function(x) length(x[,
"yi"])))
group_no <- length(unique(mod_table[, "name"]))
cbpl <- c("#E69F00", "#009E73", "#F0E442", "#0072B2", "#D55E00",
"#CC79A7", "#56B4E9", "#999999")
if (names(mod_table)[2] == "condition") {
condition_no <- length(unique(mod_table[, "condition"]))
plot <- ggplot2::ggplot() + ggbeeswarm::geom_quasirandom(data = data,
ggplot2::aes(y = yi, x = moderator, size = scale,
colour = moderator), alpha = alpha) + ggplot2::geom_hline(yintercept = 0,
linetype = 2, colour = "black", alpha = alpha) +
ggplot2::geom_linerange(data = mod_table, ggplot2::aes(x = name,
ymin = lowerCL, ymax = upperCL), size = branch.size,
position = ggplot2::position_dodge2(width = 0.3)) +
ggplot2::geom_pointrange(data = mod_table, ggplot2::aes(y = estimate,
x = name, ymin = lowerPR, ymax = upperPR, shape = as.factor(condition),
fill = name), size = 0.5, position = ggplot2::position_dodge2(width = 0.3)) +
ggplot2::scale_shape_manual(values = 20 + (1:condition_no)) +
ggplot2::coord_flip() + ggplot2::theme_bw() + ggplot2::guides(fill = "none",
colour = "none") + ggplot2::theme(legend.position = c(0,
1), legend.justification = c(0, 1)) + ggplot2::theme(legend.title = ggplot2::element_text(size = 9)) +
ggplot2::theme(legend.direction = "horizontal") +
ggplot2::theme(legend.background = ggplot2::element_blank()) +
ggplot2::labs(y = label, x = "", size = legend) +
ggplot2::scale_size_continuous(breaks = c(200, 2000, 20000), guide = guide_legend()) +
ggplot2::labs(shape = condition.lab) + ggplot2::theme(axis.text.y = ggplot2::element_text(size = 10,
colour = "black", hjust = 0.5, angle = angle))
plot <- plot + ggplot2::annotate("text", y = (max(data$yi) +
(max(data$yi) * 0.1)), x = (seq(1, group_no, 1) +
0.3), label = paste("italic(k)==", mod_table$K[1:group_no]),
parse = TRUE, hjust = "right", size = 3.5)
}
else {
plot <- ggplot2::ggplot(data = mod_table, ggplot2::aes(x = estimate,
y = name)) + ggbeeswarm::geom_quasirandom(data = data,
ggplot2::aes(x = yi, y = moderator, size = scale,
colour = moderator), groupOnX = FALSE, alpha = alpha) +
ggplot2::geom_errorbarh(ggplot2::aes(xmin = lowerPR,
xmax = upperPR), height = 0, show.legend = FALSE,
size = 0.5, alpha = 0.6) + ggplot2::geom_errorbarh(ggplot2::aes(xmin = lowerCL,
xmax = upperCL), height = 0, show.legend = FALSE,
size = branch.size) + ggplot2::geom_vline(xintercept = 0,
linetype = 2, colour = "black", alpha = alpha) +
ggplot2::geom_point(ggplot2::aes(fill = name), size = point.size,
shape = 21) + ggplot2::theme_bw() + ggplot2::guides(fill = "none",
colour = "none") + ggplot2::theme(legend.position = c(1,
0), legend.justification = c(1, 0)) + ggplot2::theme(legend.title = ggplot2::element_text(size = 9)) +
ggplot2::theme(legend.direction = "horizontal") +
ggplot2::theme(legend.background = ggplot2::element_blank()) +
ggplot2::labs(x = label, y = "", size = legend) +
ggplot2::scale_size_continuous(breaks = c(200, 2000, 20000), guide = guide_legend()) +
ggplot2::theme(axis.text.y = ggplot2::element_text(size = 10,
colour = "black", hjust = 0.5, angle = angle))
if (k == TRUE) {
plot <- plot + ggplot2::annotate("text", x = (max(data$yi) +
(max(data$yi) * 0.1)), y = (seq(1, group_no,
1) + 0.3), label = paste("italic(k)==", mod_table$K),
parse = TRUE, hjust = "right", size = 3.5)
}
}
if (cb == TRUE) {
plot <- plot + ggplot2::scale_fill_manual(values = cbpl) +
ggplot2::scale_colour_manual(values = cbpl)
}
if (legend.on == FALSE){
plot <- plot + ggplot2::theme(legend.position = "none")
}
return(plot)
}
# mod_result old
#' @title get_est
#' @description Function gets estimates from rma objects (metafor)
#' @param model rma.mv object
#' @param mod the name of a moderator. If meta-analysis (i.e. no moderator, se mod = "Int")
#' @author Shinichi Nakagawa - s.nakagawa@unsw.edu.au
#' @author Daniel Noble - daniel.noble@anu.edu.au
#' @export
get_est <- function (model, mod) {
name <- firstup(as.character(stringr::str_replace(row.names(model$beta), {{mod}}, "")))
estimate <- as.numeric(model$beta)
lowerCL <- model$ci.lb
upperCL <- model$ci.ub
table <- tibble::tibble(name = factor(name, levels = name, labels = name), estimate = estimate, lowerCL = lowerCL, upperCL = upperCL)
return(table)
}
#' @title get_pred
#' @description Function to get prediction intervals (crediblity intervals) from rma objects (metafor)
#' @param model rma.mv object
#' @param mod the name of a moderator
#' @author Shinichi Nakagawa - s.nakagawa@unsw.edu.au
#' @author Daniel Noble - daniel.noble@anu.edu.au
#' @export
get_pred <- function (model, mod) {
name <- firstup(as.character(stringr::str_replace(row.names(model$beta), {{mod}}, "")))
len <- length(name)
if(len != 1){
newdata <- matrix(NA, ncol = len, nrow = len)
pred <- metafor::predict.rma(model, newmods = diag(len),
tau2.levels = 1:len,
gamma2.levels = 1:len)
}
else {
pred <- metafor::predict.rma(model)
}
lowerPR <- pred$cr.lb
upperPR <- pred$cr.ub
table <- tibble::tibble(name = factor(name, levels = name, labels = name), lowerPR = lowerPR, upperPR = upperPR)
return(table)
}
#' @title firstup
#' @description Uppercase moderator names
#' @param x a character string
#' @author Shinichi Nakagawa - s.nakagawa@unsw.edu.au
#' @author Daniel Noble - daniel.noble@anu.edu.au
#' @return Returns a character string with all combinations of the moderator level names with upper case first letters
#' @export
firstup <- function(x) {
substr(x, 1, 1) <- toupper(substr(x, 1, 1))
x
}
#' @title get_data
#' @description Collects and builds the data used to fit the rma.mv or rma model in metafor
#' @param model rma.mv object
#' @param mod the moderator variable
#' @author Shinichi Nakagawa - s.nakagawa@unsw.edu.au
#' @author Daniel Noble - daniel.noble@anu.edu.au
#' @return Returns a data frame
#' @export
#'
get_data <- function(model, mod){
X <- as.data.frame(model$X)
names <- vapply(stringr::str_split(colnames(X), {{mod}}), function(x) paste(unique(x), collapse = ""), character(1L))
moderator <- matrix(ncol = 1, nrow = dim(X)[1])
for(i in 1:ncol(X)){
moderator <- ifelse(X[,i] == 1, names[i], moderator)
}
moderator <- firstup(moderator)
yi <- model$yi
vi <- model$vi
type <- attr(model$yi, "measure")
data <- data.frame(yi, vi, moderator, type)
return(data)
}
#' @title mod_results
#' @description Using a metafor model object of class rma or rma.mv it creates a table of model results containing the mean effect size estimates for all levels of a given categorical moderator, their corresponding confidence intervals and prediction intervals
#' @param model rma.mv object
#' @param mod the name of a moderator; put "Int" if the intercept model (meta-analysis) or no moderators.
#' @return A data frame containing all the model results including mean effect size estimate, confidence and prediction intervals
#' @author Shinichi Nakagawa - s.nakagawa@unsw.edu.au
#' @author Daniel Noble - daniel.noble@anu.edu.au
#' @examples
#' \dontrun{data(eklof)
#' eklof<-metafor::escalc(measure="ROM", n1i=N_control, sd1i=SD_control,
#' m1i=mean_control, n2i=N_treatment, sd2i=SD_treatment, m2i=mean_treatment,
#' data=eklof)
#' # Add the unit level predictor
#' eklof$Datapoint<-as.factor(seq(1, dim(eklof)[1], 1))
#' # fit a MLMR - accouting for some non-independence
#' eklof_MR<-metafor::rma.mv(yi=yi, V=vi, mods=~ Grazer.type-1, random=list(~1|ExptID,
#' ~1|Datapoint), data=eklof)
#' results <- mod_results(eklof_MR, mod = "Grazer.type")
#' }
#' @export
mod_results <- function(model, mod) {
if(all(class(model) %in% c("rma.mv", "rma.uni", "rma")) == FALSE) {stop("Sorry, you need to fit a metafor model of class rma.mv or rma")}
data <- get_data(model, mod)
# Get confidence intervals
CI <- get_est(model, mod)
# Get prediction intervals
PI <- get_pred(model, mod)
model_results <- list(mod_table = cbind(CI, PI[,-1]), data = data)
class(model_results) <- "orchard"
return(model_results)
}
# TODO - I think we can improve `mod` bit?
#' @title print.orchard
#' @description Print method for class 'orchard'
#' @param object x an R object of class orchard
#' @param ... Other arguments passed to print
#' @author Shinichi Nakagawa - s.nakagawa@unsw.edu.au
#' @author Daniel Noble - daniel.noble@anu.edu.au
#' @return Returns a data frame
#' @export
#'
print.orchard <- function(object, ...){
return(object$mod_table)
}Loading raw data and creating a list of trait data
Below we see sub-strain information and sample size for each sub-strains
# loading data
allometry <- readRDS(here("data/allometryNEW.rds"))
#STEP 1 remove rows with missing data and NA
allometrynew<-allometry[complete.cases(allometry),]
# getting rid of NA for data_point and weight
allometrynew2 <- allometrynew %>%
filter(!is.na(data_point), !is.na(weight)) %>%
group_by(parameter_name, sex, metadata_group, strain_name) %>%
mutate(count = n()) %>%
ungroup() %>%
group_by(parameter_name) %>% # adjusting interval data
mutate(min_val = min(data_point),
data_point2 = if_else(min_val > 0, data_point, data_point + abs(min_val)),
min_val2 = min( data_point[data_point!=min(data_point)]),
data_point2 = if_else(min_val == 0, data_point2 + min_val2, data_point2),
ratio_int = if_else(min_val > 0, "ratio", "interval"),
new_min = min(data_point2),
nmeta = n_distinct(metadata_group),
nstrain = n_distinct(strain_name),
sex = as.factor(sex),
parameter_name = if_else(parameter_name == "Latency to fall_Mean",
"Latency to fall mean" , parameter_name)) %>%
ungroup() %>%
filter(count > 49) %>% # this can be adjusted
filter(parameter_name != "BMC/Body weight",
parameter_name != "Body weight",
parameter_name != "Body Weight",
parameter_name != "Body weight after experiment" ,
parameter_name != "Body weight before experiment",
parameter_name != "Test duration") %>%
filter(!is.infinite(data_point2), !is.infinite(log(data_point2))) # removing infite and 0
# dim(allometry)
# dim(allometrynew)
# dim(allometrynew2)
#
# # the number of traits
# length(unique(allometrynew2$parameter_name))
#
# # the number of substrains
# length(unique(allometrynew2$strain_name))
# strain information
summary(factor(allometrynew2$strain_name))## B6Brd;B6Dnk;B6N-Tyr<c-Brd> C57BL/6N
## 10395 568417
## C57BL/6N;C57BL/6NTac C57BL/6NCrl
## 57663 327250
## C57BL/6NJ C57BL/6NJcl
## 312539 33370
## C57BL/6NTac
## 808736
# # check there is no 0
# sum(is.infinite(log(allometrynew2$data_point2)))
# # the number of interval scale traits
# allometrynew2 %>% group_by(parameter_name) %>% summarise(ratio_int = ratio_int[1]) -> sum_ri
# sum(sum_ri$ratio_int == "interval")
#split dataframe by parameter to generate a list of dfs
#all_list<-split(allometrynew2, allometrynew2$parameter_name)
#saveRDS(all_list, file = here("data", "dat_list2.rds"))Loading a list of trait data and group category
# loading data
dat_list <- readRDS(here("data/dat_list2.rds"))
# grouping for category and parameter_group (this is from Zajitschek et al.
# 2020 eLife; slightly modfied)
dat_category <- read_csv(here("data/cateogry_parameter3.csv"))Data preparation
Obtaining intersepcts, slopes, residual SDs and model fits
#run function across list of matrices
processing <-map_dfr(dat_list2, get_para_poss)
dat <- data.frame(processing, row.names = NULL)
dat %>% left_join(dat_category, by = ("parameter_name" = "parameter_name") ) %>% arrange(Category) -> dat
dim(dat)
#write_csv(dat, here("data/test4.csv"))
write_csv(dat, here("data/data_parameters5.csv"))
# first getting p values - the contrasts between males and females for
dat <-read_csv(here("data/data_parameters5.csv"))
#assess number of traits with sig shifts in intercept and slope
# getting lnVR to compare SDs and SD and Zr (variance for Zr)
dat %>% mutate(lnVR = log(f_sd/m_sd) + 1/(2*(f_n-3)) - 1/(2*(m_n-3)),
VlnVR = 1/(2*(f_n-3)) + 1/(2*(m_n-3)),
low_lnVR = lnVR - qnorm(0.975)*VlnVR,
high_lnVR = lnVR + qnorm(0.975)*VlnVR,
t_val_sd = lnVR/sqrt(VlnVR),
p_val_sd = 2*(1-pt(abs(t_val_sd), f_n-1 + m_n-1)),
# r squared
Zr = atanh(r_m),
VZr = 1/((f_n + m_n) - 3)
) -> dat
write_csv(dat, here("data/data_parameters6.csv"))Dataset and meta-data
# loading data
dat <- read_csv(here("data/data_parameters6.csv"))
# creating observation level random effect
dat$obs <- 1:dim(dat)[1]
# making character strings into factors
dat <- dat %>%
mutate_if(is.character, as.factor)
# visualizing
kable(dat, "html") %>%
kable_styling("striped", position = "left") %>%
scroll_box(width = "100%", height = "250px")| parameter_name | procedure_name | f_n | m_n | f_intercept | f_intercept_se | f_slope | f_slope_se | m_intercept | m_intercept_se | m_slope | m_slope_se | fm_diff_int | fm_diff_int_se | fm_diff_int_p | fm_diff_slope | fm_diff_slope_se | fm_diff_slope_p | f_sd | m_sd | r_m | r_c | Category | parameter_group | lnVR | VlnVR | low_lnVR | high_lnVR | t_val_sd | p_val_sd | Zr | VZr | obs |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| activity onset with respect to dark onset median | Sleep Wake | 273 | 259 | 0.3997494 | 0.0265681 | -0.0641795 | 0.3438944 | 0.2389278 | 0.0241286 | -0.1376488 | 0.2585014 | 0.1608216 | 0.0287473 | 0.0000000 | 0.0734692 | 0.4204250 | 0.8613509 | 0.3593388 | 0.2969758 | 0.2241775 | 0.3920506 | Behaviour | activity onset with respect to dark onset median | 0.1905138 | 0.0038050 | 0.1830562 | 0.1979714 | 3.0885202 | 0.0021170 | 0.2280504 | 0.0018904 | 1 |
| average duration | Rotarod | 621 | 612 | 4.1396747 | 0.0534919 | -0.4230798 | 0.5306746 | 4.0174141 | 0.0544484 | -0.3654569 | 0.5312267 | 0.1222607 | 0.0310508 | 0.0000873 | -0.0576229 | 0.3396141 | 0.8652985 | 0.5134660 | 0.5715872 | 0.1198498 | 0.4012784 | Behaviour | average duration | -0.1072451 | 0.0016301 | -0.1104400 | -0.1040502 | -2.6562746 | 0.0080030 | 0.1204287 | 0.0008130 | 2 |
| breath rate during sleep mean | Sleep Wake | 864 | 842 | 0.9449821 | 0.0044193 | 0.2082084 | 0.0330988 | 1.0332861 | 0.0042531 | 0.2132866 | 0.0262782 | -0.0883040 | 0.0034163 | 0.0000000 | -0.0050782 | 0.0379381 | 0.8935342 | 0.0748341 | 0.0649490 | 0.5035914 | 0.6860983 | Behaviour | breath rate during sleep mean | 0.1416557 | 0.0011767 | 0.1393495 | 0.1439619 | 4.1295925 | 0.0000381 | 0.5541062 | 0.0005872 | 3 |
| breath rate during sleep standard deviation | Sleep Wake | 864 | 842 | -0.2498343 | 0.0050103 | 0.0047054 | 0.0456216 | -0.2151047 | 0.0049305 | 0.0533077 | 0.0386388 | -0.0347296 | 0.0050356 | 0.0000000 | -0.0486024 | 0.0552482 | 0.3791537 | 0.1057847 | 0.1010671 | 0.1568210 | 0.4112851 | Behaviour | breath rate during sleep standard deviation | 0.0456060 | 0.0011767 | 0.0432998 | 0.0479122 | 1.3295208 | 0.1838542 | 0.1581258 | 0.0005872 | 4 |
| center average speed | Open Field | 8692 | 8660 | 2.3458633 | 0.2348120 | -0.0223914 | 0.0827329 | 2.2616138 | 0.2348157 | 0.0289145 | 0.0820327 | 0.0842495 | 0.0043120 | 0.0000000 | -0.0513058 | 0.0398533 | 0.1979860 | 0.2621181 | 0.2641308 | 0.0490904 | 0.9520402 | Behaviour | center average speed | -0.0076493 | 0.0001153 | -0.0078753 | -0.0074233 | -0.7123706 | 0.4762449 | 0.0491299 | 0.0000576 | 5 |
| center distance travelled | Open Field | 9025 | 8992 | 7.2534777 | 0.2882776 | -0.1519985 | 0.1439632 | 7.1888348 | 0.2882827 | -0.0260257 | 0.1405545 | 0.0646429 | 0.0090684 | 0.0000000 | -0.1259729 | 0.0841550 | 0.1344356 | 0.5689508 | 0.5352490 | 0.0292466 | 0.8806315 | Behaviour | center distance travelled | 0.0610617 | 0.0001110 | 0.0608440 | 0.0612793 | 5.7945779 | 0.0000000 | 0.0292549 | 0.0000555 | 6 |
| center permanence time | Open Field | 9381 | 9328 | 5.0861025 | 0.1426084 | -0.0768003 | 0.1231307 | 5.1177051 | 0.1426655 | -0.0260283 | 0.1159681 | -0.0316026 | 0.0099742 | 0.0015355 | -0.0507720 | 0.0928835 | 0.5846471 | 0.6505803 | 0.5862078 | 0.0192793 | 0.7023580 | Behaviour | center time | 0.1041902 | 0.0001069 | 0.1039806 | 0.1043998 | 10.0754775 | 0.0000000 | 0.0192816 | 0.0000535 | 7 |
| center resting time | Open Field | 6471 | 6443 | 3.2297243 | 0.3054032 | 0.0410097 | 0.2857014 | 3.2675150 | 0.3053440 | -0.0799191 | 0.2743587 | -0.0377907 | 0.0199906 | 0.0587261 | 0.1209289 | 0.2131361 | 0.5704681 | 1.0512567 | 0.9515445 | 0.0132175 | 0.7381527 | Behaviour | center time | 0.0996548 | 0.0001549 | 0.0993512 | 0.0999585 | 8.0059312 | 0.0000000 | 0.0132183 | 0.0000775 | 8 |
| conditioning baseline % freezing time | Fear Conditioning | 168 | 238 | 1.9687958 | 0.1356950 | -1.1332900 | 1.2991688 | 1.9635375 | 0.1204710 | -1.0243329 | 1.0938559 | 0.0052583 | 0.1196737 | 0.9649796 | -0.1089570 | 1.6596716 | 0.9476964 | 0.9855756 | 0.9794872 | 0.0586042 | 0.6517738 | Behaviour | conditioning baseline freezing time | 0.0070994 | 0.0051580 | -0.0030101 | 0.0172088 | 0.0988507 | 0.9213058 | 0.0586714 | 0.0024814 | 9 |
| conditioning baseline average motion index | Fear Conditioning | 168 | 238 | 4.5314786 | 0.0863724 | 0.8355070 | 0.6100651 | 4.4596285 | 0.0818414 | 0.0323033 | 0.5798292 | 0.0718501 | 0.0601345 | 0.2330144 | 0.8032037 | 0.8201500 | 0.3281306 | 0.4371868 | 0.5098784 | 0.0675720 | 0.7970570 | Behaviour | conditioning baseline average motion index | -0.1529091 | 0.0051580 | -0.1630185 | -0.1427997 | -2.1290913 | 0.0338509 | 0.0676751 | 0.0024814 | 10 |
| conditioning baseline freeze count | Fear Conditioning | 291 | 333 | 1.3256866 | 0.3883915 | -0.8856871 | 0.7413944 | 1.3529550 | 0.3880310 | -1.3567942 | 0.7655466 | -0.0272684 | 0.0838470 | 0.7451497 | 0.4711072 | 1.0479255 | 0.6532157 | 0.7753470 | 0.8508352 | 0.0778264 | 0.6787820 | Behaviour | conditioning baseline freeze count | -0.0926869 | 0.0032513 | -0.0990593 | -0.0863146 | -1.6255204 | 0.1045583 | 0.0779841 | 0.0016103 | 11 |
| conditioning baseline freezing time | Fear Conditioning | 291 | 333 | 1.2217029 | 0.7852620 | -1.0534374 | 1.0137547 | 1.2255487 | 0.7849207 | -1.5067021 | 1.0670809 | -0.0038458 | 0.1169878 | 0.9737881 | 0.4532647 | 1.4457104 | 0.7540097 | 1.0434934 | 1.1677198 | 0.0547520 | 0.7767253 | Behaviour | conditioning baseline freezing time | -0.1122579 | 0.0032513 | -0.1186302 | -0.1058855 | -1.9687513 | 0.0494252 | 0.0548068 | 0.0016103 | 12 |
| conditioning baseline maximum motion index | Fear Conditioning | 168 | 238 | 6.7808435 | 0.0439214 | 1.1742140 | 0.4081083 | 6.8121511 | 0.0399717 | 0.1367413 | 0.3690367 | -0.0313076 | 0.0389738 | 0.4223796 | 1.0374728 | 0.5376755 | 0.0545194 | 0.3055010 | 0.3349078 | 0.1278045 | 0.6598784 | Behaviour | conditioning baseline maximum motion index | -0.0909995 | 0.0051580 | -0.1011089 | -0.0808901 | -1.2670678 | 0.2058610 | 0.1285072 | 0.0024814 | 13 |
| conditioning post-shock % freezing time | Fear Conditioning | 168 | 238 | 3.3147621 | 0.0775739 | -1.5838735 | 0.7880700 | 3.2917899 | 0.0681076 | -0.7990046 | 0.6652213 | 0.0229722 | 0.0726002 | 0.7518831 | -0.7848689 | 1.0091270 | 0.4372617 | 0.6048891 | 0.6061758 | 0.1103729 | 0.6067092 | Behaviour | conditioning post-shock freezing time | -0.0012223 | 0.0051580 | -0.0113317 | 0.0088871 | -0.0170191 | 0.9864298 | 0.1108244 | 0.0024814 | 14 |
| conditioning post-shock average motion index | Fear Conditioning | 168 | 238 | 3.8928410 | 0.1054836 | 2.0402110 | 0.9817725 | 3.9305376 | 0.0922612 | 0.8854406 | 0.7507643 | -0.0376965 | 0.0866729 | 0.6638982 | 1.1547704 | 1.2069869 | 0.3393998 | 0.7496078 | 0.6490194 | 0.1079650 | 0.7026911 | Behaviour | conditioning post-shock average motion index | 0.1449902 | 0.0051580 | 0.1348807 | 0.1550996 | 2.0188286 | 0.0441652 | 0.1083874 | 0.0024814 | 15 |
| conditioning post-shock freeze count | Fear Conditioning | 291 | 333 | 2.6904541 | 0.2298969 | -1.5148027 | 0.6510377 | 2.6300944 | 0.2293120 | -1.2451349 | 0.6216493 | 0.0603597 | 0.0591335 | 0.3078586 | -0.2696678 | 0.7652330 | 0.7246828 | 0.6264732 | 0.6200001 | 0.1382949 | 0.5676127 | Behaviour | conditioning post-shock freeze count | 0.0106073 | 0.0032513 | 0.0042349 | 0.0169796 | 0.1860276 | 0.8524837 | 0.1391868 | 0.0016103 | 16 |
| conditioning post-shock freezing time | Fear Conditioning | 291 | 333 | 3.1947735 | 0.5237483 | -1.7771914 | 0.7717368 | 3.0855839 | 0.5232260 | -1.3484129 | 0.7284680 | 0.1091896 | 0.0813590 | 0.1801643 | -0.4287785 | 1.0387322 | 0.6799321 | 0.8331076 | 0.8221438 | 0.1062917 | 0.7171245 | Behaviour | conditioning post-shock freezing time | 0.0134685 | 0.0032513 | 0.0070961 | 0.0198409 | 0.2362072 | 0.8133496 | 0.1066947 | 0.0016103 | 17 |
| conditioning post-shock maximum motion index | Fear Conditioning | 168 | 238 | 6.9942827 | 0.0602561 | 0.7897629 | 0.7305179 | 7.1462052 | 0.0489809 | 0.6013278 | 0.5686143 | -0.1519225 | 0.0646968 | 0.0194532 | 0.1884351 | 0.9111762 | 0.8362903 | 0.5932629 | 0.5469635 | 0.1438784 | 0.4382333 | Behaviour | conditioning post-shock maximum motion index | 0.0821582 | 0.0051580 | 0.0720488 | 0.0922676 | 1.1439622 | 0.2533166 | 0.1448838 | 0.0024814 | 18 |
| conditioning shock average motion index | Fear Conditioning | 168 | 238 | 6.4573870 | 0.0452897 | 0.8237100 | 0.4393696 | 6.6813122 | 0.0391660 | 0.3853583 | 0.3374222 | -0.2239252 | 0.0388448 | 0.0000000 | 0.4383517 | 0.5413804 | 0.4187004 | 0.3375430 | 0.2949573 | 0.2707497 | 0.6968581 | Behaviour | conditioning shock average motion index | 0.1357650 | 0.0051580 | 0.1256556 | 0.1458744 | 1.8903785 | 0.0594228 | 0.2776727 | 0.0024814 | 19 |
| conditioning shock maximum motion index | Fear Conditioning | 168 | 238 | 7.6269920 | 0.0409471 | 0.3891402 | 0.4624358 | 7.8937217 | 0.0348759 | 0.4640156 | 0.3812632 | -0.2667297 | 0.0420367 | 0.0000000 | -0.0748753 | 0.5882642 | 0.8987949 | 0.3655110 | 0.3590340 | 0.3076035 | 0.5671884 | Behaviour | conditioning shock maximum motion index | 0.0187821 | 0.0051580 | 0.0086726 | 0.0288915 | 0.2615195 | 0.7938252 | 0.3178963 | 0.0024814 | 20 |
| conditioning shock minimum motion index | Fear Conditioning | 168 | 238 | 2.1410630 | 0.1740283 | 4.6921170 | 2.1280215 | 2.3667075 | 0.1484018 | 0.0208757 | 1.7855866 | -0.2256445 | 0.1945456 | 0.2469466 | 4.6712413 | 2.7370597 | 0.0888252 | 1.7290140 | 1.7584580 | 0.1233223 | 0.4151157 | Behaviour | conditioning shock minimum motion index | -0.0159833 | 0.0051580 | -0.0260928 | -0.0058739 | -0.2225504 | 0.8239979 | 0.1239533 | 0.0024814 | 21 |
| conditioning tone % freezing time | Fear Conditioning | 168 | 238 | 2.6592328 | 0.1056836 | -0.0481380 | 1.2194692 | 2.5334482 | 0.0868385 | -0.6496135 | 0.9455468 | 0.1257846 | 0.1079399 | 0.2447310 | 0.6014755 | 1.5152886 | 0.6916698 | 0.9739933 | 0.8838882 | 0.0701445 | 0.4899381 | Behaviour | conditioning tone freezing time | 0.0979765 | 0.0051580 | 0.0878671 | 0.1080860 | 1.3642155 | 0.1732592 | 0.0702599 | 0.0024814 | 22 |
| conditioning tone average motion index | Fear Conditioning | 168 | 238 | 4.1522916 | 0.1129142 | 1.2659456 | 1.1647512 | 4.3137044 | 0.0940875 | -0.1306228 | 0.8388731 | -0.1614128 | 0.1003538 | 0.1086958 | 1.3965684 | 1.4045847 | 0.3208089 | 0.9109603 | 0.7319176 | 0.0991057 | 0.6238896 | Behaviour | conditioning tone average motion index | 0.2197340 | 0.0051580 | 0.2096246 | 0.2298435 | 3.0595548 | 0.0023644 | 0.0994321 | 0.0024814 | 23 |
| conditioning tone freeze count | Fear Conditioning | 291 | 333 | 1.2526945 | 0.1688261 | -0.5098152 | 0.6652240 | 1.1506940 | 0.1682551 | -0.5389681 | 0.6521971 | 0.1020004 | 0.0655347 | 0.1202193 | 0.0291528 | 0.8488130 | 0.9726150 | 0.6796764 | 0.7043444 | 0.0799076 | 0.4783658 | Behaviour | conditioning tone freeze count | -0.0354297 | 0.0032513 | -0.0418021 | -0.0290574 | -0.6213581 | 0.5345917 | 0.0800784 | 0.0016103 | 24 |
| conditioning tone freezing time | Fear Conditioning | 291 | 333 | 1.2318936 | 0.1745432 | -0.8374521 | 1.0630281 | 1.0366505 | 0.1715189 | -0.7934700 | 1.0184930 | 0.1952430 | 0.1134222 | 0.0857820 | -0.0439821 | 1.4509990 | 0.9758303 | 1.1543876 | 1.1638808 | 0.0866580 | 0.4698373 | Behaviour | conditioning tone freezing time | -0.0079690 | 0.0032513 | -0.0143413 | -0.0015966 | -0.1397580 | 0.8888964 | 0.0868759 | 0.0016103 | 25 |
| conditioning tone maximum motion index | Fear Conditioning | 168 | 238 | 6.3288570 | 0.0681134 | 0.8874206 | 0.7298403 | 6.4400652 | 0.0576131 | -0.4852241 | 0.5755577 | -0.1112082 | 0.0651566 | 0.0888032 | 1.3726448 | 0.9104601 | 0.1326038 | 0.5705884 | 0.5233730 | 0.1120496 | 0.5730043 | Behaviour | conditioning tone maximum motion index | 0.0872763 | 0.0051580 | 0.0771669 | 0.0973857 | 1.2152262 | 0.2249895 | 0.1125221 | 0.0024814 | 26 |
| context % freezing time | Fear Conditioning | 168 | 238 | 3.7953473 | 0.0477961 | -0.8334483 | 0.5399609 | 3.7641734 | 0.0411358 | 0.0962053 | 0.4543183 | 0.0311739 | 0.0495326 | 0.5295487 | -0.9296536 | 0.6927444 | 0.1805230 | 0.4262883 | 0.4303336 | 0.0823129 | 0.4988817 | Behaviour | context % freezing time | -0.0085421 | 0.0051580 | -0.0186515 | 0.0015673 | -0.1189396 | 0.9053823 | 0.0824996 | 0.0024814 | 27 |
| context average motion index | Fear Conditioning | 168 | 238 | 3.4099842 | 0.0921967 | 1.3823332 | 0.9384873 | 3.4286043 | 0.0823208 | -0.0379329 | 0.8302016 | -0.0186201 | 0.0884268 | 0.8333523 | 1.4202661 | 1.2265822 | 0.2477400 | 0.7176001 | 0.7674251 | 0.0685276 | 0.5846227 | Behaviour | context average motion index | -0.0662258 | 0.0051580 | -0.0763352 | -0.0561164 | -0.9221216 | 0.3570155 | 0.0686352 | 0.0024814 | 28 |
| context freeze count | Fear Conditioning | 291 | 333 | 3.5584471 | 0.2127340 | -0.8910161 | 0.8648738 | 3.5551432 | 0.2124420 | -1.0544304 | 0.8509927 | 0.0033039 | 0.0452987 | 0.9418848 | 0.1634143 | 0.6006142 | 0.7856706 | 0.5112218 | 0.5093275 | 0.1158975 | 0.5627246 | Behaviour | context freeze count | 0.0039333 | 0.0032513 | -0.0024391 | 0.0103056 | 0.0689811 | 0.9450269 | 0.1164207 | 0.0016103 | 29 |
| context freezing time | Fear Conditioning | 291 | 333 | 4.2581394 | 0.6306685 | -1.0683928 | 0.6946594 | 4.2325694 | 0.6304768 | -0.5739640 | 0.6660883 | 0.0255700 | 0.0604756 | 0.6726059 | -0.4944288 | 0.7874042 | 0.5303331 | 0.6504133 | 0.6469207 | 0.0550703 | 0.8202126 | Behaviour | context freezing time | 0.0056053 | 0.0032513 | -0.0007671 | 0.0119776 | 0.0983040 | 0.9217226 | 0.0551260 | 0.0016103 | 30 |
| context maximum motion index | Fear Conditioning | 168 | 238 | 6.6023528 | 0.0481364 | 1.1907436 | 0.4991537 | 6.6168804 | 0.0442601 | -0.2300421 | 0.4741404 | -0.0145275 | 0.0487445 | 0.7658643 | 1.4207856 | 0.6748070 | 0.0360058 | 0.3812654 | 0.4496842 | 0.1072734 | 0.5536005 | Behaviour | context maximum motion index | -0.1641473 | 0.0051580 | -0.1742567 | -0.1540379 | -2.2855706 | 0.0227966 | 0.1076877 | 0.0024814 | 31 |
| cue baseline % freezing time | Fear Conditioning | 168 | 238 | 2.3513883 | 0.1172587 | -2.4651670 | 1.1577325 | 2.5281740 | 0.1053134 | -2.2215091 | 1.0294683 | -0.1767857 | 0.1094571 | 0.1072417 | -0.2436579 | 1.5155514 | 0.8723715 | 0.8787548 | 0.9444639 | 0.1592520 | 0.6219508 | Behaviour | cue baseline % freezing time | -0.0712089 | 0.0051580 | -0.0813183 | -0.0610995 | -0.9915052 | 0.3220324 | 0.1606191 | 0.0024814 | 32 |
| cue baseline average motion index | Fear Conditioning | 168 | 238 | 4.6147048 | 0.0738768 | 1.6704806 | 0.6730765 | 4.5085955 | 0.0698425 | 1.4888235 | 0.6839540 | 0.1061093 | 0.0686049 | 0.1229005 | 0.1816570 | 0.9387587 | 0.8466804 | 0.4960040 | 0.6381056 | 0.1636089 | 0.6282879 | Behaviour | cue baseline average motion index | -0.2510172 | 0.0051580 | -0.2611266 | -0.2409078 | -3.4951386 | 0.0005263 | 0.1650926 | 0.0024814 | 33 |
| cue baseline freeze count | Fear Conditioning | 291 | 333 | 2.4628246 | 0.4280553 | -1.8969325 | 0.7534559 | 2.5967299 | 0.4277849 | -1.0244725 | 0.7691788 | -0.1339053 | 0.0663870 | 0.0442096 | -0.8724600 | 0.8215435 | 0.2887440 | 0.6003054 | 0.6560492 | 0.1293175 | 0.7720346 | Behaviour | cue baseline freeze count | -0.0885763 | 0.0032513 | -0.0949487 | -0.0822039 | -1.5534293 | 0.1208292 | 0.1300457 | 0.0016103 | 34 |
| cue baseline freezing time | Fear Conditioning | 291 | 333 | 2.7603273 | 0.2271141 | -2.5255004 | 0.7613095 | 2.9315484 | 0.2264336 | -1.7657152 | 0.8052562 | -0.1712211 | 0.0857489 | 0.0463757 | -0.7597852 | 1.0595377 | 0.4736429 | 0.7605435 | 0.8585249 | 0.1654348 | 0.6549631 | Behaviour | cue baseline freezing time | -0.1209614 | 0.0032513 | -0.1273337 | -0.1145890 | -2.1213908 | 0.0342838 | 0.1669694 | 0.0016103 | 35 |
| cue baseline maximum motion index | Fear Conditioning | 168 | 238 | 7.1268980 | 0.0394385 | 0.7307158 | 0.4459261 | 7.1110478 | 0.0354993 | 0.2950239 | 0.4066379 | 0.0158501 | 0.0424959 | 0.7094021 | 0.4356919 | 0.5929337 | 0.4629788 | 0.3505362 | 0.3935991 | 0.0877404 | 0.4727205 | Behaviour | cue baseline maximum motion index | -0.1149662 | 0.0051580 | -0.1250756 | -0.1048567 | -1.6007775 | 0.1102078 | 0.0879666 | 0.0024814 | 36 |
| cue tone % freezing time | Fear Conditioning | 168 | 238 | 3.4820511 | 0.0798422 | -2.4839656 | 0.8738825 | 3.5334856 | 0.0659951 | -0.6397100 | 0.6600543 | -0.0514345 | 0.0766177 | 0.5024905 | -1.8442556 | 1.0732718 | 0.0866709 | 0.6892142 | 0.5973727 | 0.1496055 | 0.5705014 | Behaviour | cue tone % freezing time | 0.1439135 | 0.0051580 | 0.1338041 | 0.1540229 | 2.0038374 | 0.0457547 | 0.1507369 | 0.0024814 | 37 |
| dark side distance travelled | Light-Dark Test | 111 | 84 | 8.1103219 | 0.0412207 | 0.0478326 | 0.6114389 | 8.1080642 | 0.0380672 | 0.6952289 | 0.4687043 | 0.0022577 | 0.0455400 | 0.9605232 | -0.6473963 | 0.7565298 | 0.3934432 | 0.3355864 | 0.2443481 | 0.0929921 | 0.4230523 | Behaviour | distance travelled | 0.3157423 | 0.0108025 | 0.2945698 | 0.3369147 | 3.0378840 | 0.0027122 | 0.0932615 | 0.0052083 | 38 |
| dark side time spent | Light-Dark Test | 1844 | 1791 | 6.5989204 | 0.2352543 | 0.2280163 | 0.2488061 | 6.4640226 | 0.2354316 | 0.2646193 | 0.2552859 | 0.1348979 | 0.0143157 | 0.0000000 | -0.0366030 | 0.1273903 | 0.7738784 | 0.3455629 | 0.5006584 | 0.1263517 | 0.6609182 | Behaviour | time spent | -0.3707575 | 0.0005512 | -0.3718379 | -0.3696771 | -15.7914520 | 0.0000000 | 0.1270306 | 0.0002753 | 39 |
| dark sleep bout lengths mean | Sleep Wake | 864 | 842 | 5.2571726 | 0.0090871 | 0.3303906 | 0.0910267 | 5.5833384 | 0.0100440 | 0.6787470 | 0.0895659 | -0.3261658 | 0.0112709 | 0.0000000 | -0.3483564 | 0.1212856 | 0.0041321 | 0.2158973 | 0.2465996 | 0.5793367 | 0.6223104 | Behaviour | dark sleep bout lengths mean | -0.1329785 | 0.0011767 | -0.1352848 | -0.1306723 | -3.8766329 | 0.0001099 | 0.6614637 | 0.0005872 | 40 |
| dark sleep bout lengths standard deviation | Sleep Wake | 864 | 842 | 5.6281410 | 0.0122338 | 0.1542173 | 0.1155083 | 5.8580053 | 0.0120277 | 0.0984854 | 0.0978531 | -0.2298643 | 0.0128769 | 0.0000000 | 0.0557319 | 0.1409984 | 0.6927013 | 0.2704850 | 0.2587710 | 0.3787037 | 0.4972531 | Behaviour | dark sleep bout lengths standard deviation | 0.0442578 | 0.0011767 | 0.0419515 | 0.0465640 | 1.2902167 | 0.1971505 | 0.3985454 | 0.0005872 | 41 |
| data confidence level | Sleep Wake | 864 | 842 | -0.0214007 | 0.0021128 | 0.0423854 | 0.0204318 | -0.0053060 | 0.0015451 | 0.0244912 | 0.0107039 | -0.0160947 | 0.0019274 | 0.0000000 | 0.0178942 | 0.0217314 | 0.4103918 | 0.0499253 | 0.0258886 | 0.1984623 | 0.4414541 | Behaviour | data confidence level | 0.6567092 | 0.0011767 | 0.6544030 | 0.6590154 | 19.1445961 | 0.0000000 | 0.2011313 | 0.0005872 | 42 |
| distance travelled - total | Open Field | 8942 | 8881 | 8.9019575 | 0.2110965 | -0.0859905 | 0.1164261 | 8.8008435 | 0.2110980 | -0.0463850 | 0.1159899 | 0.1011140 | 0.0034070 | 0.0000000 | -0.0396055 | 0.0315352 | 0.2091657 | 0.2084779 | 0.1990104 | 0.0665909 | 0.9639911 | Behaviour | distance travelled - total | 0.0464753 | 0.0001123 | 0.0462553 | 0.0466953 | 4.3865370 | 0.0000116 | 0.0666896 | 0.0000561 | 43 |
| fecal boli | Light-Dark Test | 1504 | 1489 | 0.6319672 | 0.0196275 | -0.5037837 | 0.1773036 | 1.0838527 | 0.0224672 | -0.1256279 | 0.1757448 | -0.4518855 | 0.0262459 | 0.0000000 | -0.3781558 | 0.2355112 | 0.1084593 | 0.6531191 | 0.7751150 | 0.2986857 | 0.3596170 | Behaviour | fecal boli | -0.1712553 | 0.0006696 | -0.1725676 | -0.1699429 | -6.6182180 | 0.0000000 | 0.3080759 | 0.0003344 | 44 |
| forelimb and hindlimb grip strength measurement mean | Grip Strength | 12362 | 12416 | 5.2562639 | 0.0469759 | 0.4358817 | 0.0434866 | 5.3234999 | 0.0469773 | 0.4211003 | 0.0430899 | -0.0672360 | 0.0018728 | 0.0000000 | 0.0147814 | 0.0179420 | 0.4100371 | 0.1278248 | 0.1314747 | 0.2173814 | 0.8654272 | Behaviour | limb strength | -0.0281542 | 0.0000807 | -0.0283125 | -0.0279960 | -3.1333448 | 0.0017303 | 0.2209060 | 0.0000404 | 45 |
| forelimb and hindlimb grip strength normalised against body weight | Grip Strength | 12355 | 12405 | 2.2498816 | 0.0480931 | -0.5382140 | 0.0383410 | 2.1050333 | 0.0480945 | -0.5653667 | 0.0379372 | 0.1448483 | 0.0018667 | 0.0000000 | 0.0271527 | 0.0178859 | 0.1290019 | 0.1272733 | 0.1312920 | 0.3461161 | 0.8764899 | Behaviour | limb strength | -0.0310869 | 0.0000808 | -0.0312453 | -0.0309286 | -3.4584764 | 0.0005442 | 0.3610245 | 0.0000404 | 46 |
| forelimb grip strength measurement mean | Grip Strength | 12367 | 12430 | 4.5563487 | 0.0549138 | 0.4489343 | 0.0569998 | 4.6378744 | 0.0549158 | 0.3833404 | 0.0563995 | -0.0815257 | 0.0023514 | 0.0000000 | 0.0655939 | 0.0225559 | 0.0036405 | 0.1639006 | 0.1625589 | 0.1934478 | 0.8488125 | Behaviour | limb strength | 0.0082202 | 0.0000807 | 0.0080621 | 0.0083783 | 0.9151918 | 0.3600999 | 0.1959166 | 0.0000403 | 47 |
| forelimb grip strength normalised against body weight | Grip Strength | 12360 | 12419 | 1.5515479 | 0.0562139 | -0.5175881 | 0.0556500 | 1.4210525 | 0.0562155 | -0.5959926 | 0.0550657 | 0.1304954 | 0.0023485 | 0.0000000 | 0.0784045 | 0.0225246 | 0.0005008 | 0.1635700 | 0.1623565 | 0.2772892 | 0.8584921 | Behaviour | limb strength | 0.0074468 | 0.0000807 | 0.0072885 | 0.0076050 | 0.8287848 | 0.4072342 | 0.2847431 | 0.0000404 | 48 |
| horizontal activity | Light-Dark Test | 111 | 84 | 7.4340118 | 0.0434066 | -0.3906362 | 0.5612726 | 7.3961798 | 0.0456757 | 1.1706463 | 0.5438494 | 0.0378321 | 0.0470721 | 0.4227825 | -1.5612825 | 0.7645598 | 0.0428183 | 0.2969726 | 0.2843255 | 0.1655313 | 0.5358302 | Behaviour | horizontal activity | 0.0419769 | 0.0108025 | 0.0208045 | 0.0631494 | 0.4038770 | 0.6867503 | 0.1670685 | 0.0052083 | 49 |
| latency to center entry | Open Field | 6566 | 6537 | 2.0094981 | 0.2293746 | 0.4251788 | 0.4788778 | 2.0465107 | 0.2295697 | 0.2891182 | 0.4705180 | -0.0370127 | 0.0370393 | 0.3176796 | 0.1360606 | 0.3942555 | 0.7300197 | 1.9007446 | 1.8997163 | 0.0175040 | 0.4634366 | Behaviour | latency to center entry | 0.0005408 | 0.0001527 | 0.0002415 | 0.0008401 | 0.0437640 | 0.9650932 | 0.0175058 | 0.0000763 | 50 |
| latency to fall mean | Rotarod | 1869 | 1970 | 4.8815734 | 0.0190080 | -0.8228530 | 0.1126032 | 4.7906026 | 0.0188095 | -0.5395591 | 0.0976341 | 0.0909708 | 0.0126080 | 0.0000000 | -0.2832939 | 0.1357969 | 0.0370364 | 0.3550101 | 0.3505268 | 0.1650963 | 0.6629921 | Behaviour | latency to fall mean | 0.0127228 | 0.0005221 | 0.0116994 | 0.0137462 | 0.5567832 | 0.5777081 | 0.1666213 | 0.0002607 | 51 |
| latency to first transition into dark | Light-Dark Test | 1844 | 1791 | 2.0046284 | 1.0204016 | 0.5477040 | 0.8724567 | 2.3298420 | 1.0204588 | 0.3335784 | 0.8518543 | -0.3252136 | 0.0477445 | 0.0000000 | 0.2141256 | 0.4456709 | 0.6309346 | 1.4345227 | 1.4173880 | 0.0717907 | 0.7974235 | Behaviour | latency to first transition into dark | 0.0120084 | 0.0005512 | 0.0109280 | 0.0130888 | 0.5114656 | 0.6090562 | 0.0719145 | 0.0002753 | 52 |
| latency to immobility | Tail Suspension | 586 | 585 | 3.0708268 | 0.5111859 | -0.1047687 | 0.2633459 | 3.0682166 | 0.5111783 | -0.6541481 | 0.2566557 | 0.0026102 | 0.0192426 | 0.8921272 | 0.5493794 | 0.1913759 | 0.0041762 | 0.3334982 | 0.3238124 | 0.0627472 | 0.9143641 | Behaviour | latency to immobility | 0.0294715 | 0.0017167 | 0.0261068 | 0.0328363 | 0.7112961 | 0.4770427 | 0.0628297 | 0.0008562 | 53 |
| learning difference | Rotarod | 620 | 612 | 4.7666967 | 0.0323092 | -0.2989808 | 0.3495393 | 4.7551567 | 0.0319520 | 0.0345871 | 0.3376518 | 0.0115400 | 0.0217477 | 0.5957787 | -0.3335679 | 0.2340484 | 0.1543708 | 0.3920740 | 0.3710799 | 0.0517688 | 0.1522584 | Behaviour | learning difference | 0.0550227 | 0.0016314 | 0.0518252 | 0.0582201 | 1.3622683 | 0.1733626 | 0.0518151 | 0.0008137 | 54 |
| learning slope | Rotarod | 620 | 611 | 3.6067648 | 0.0309284 | -0.2126303 | 0.3335846 | 3.6007002 | 0.0295281 | -0.0519898 | 0.3099939 | 0.0060646 | 0.0214736 | 0.7776729 | -0.1606405 | 0.2337169 | 0.4920151 | 0.4110924 | 0.3390814 | 0.0362355 | 0.1704154 | Behaviour | learning slope | 0.1925660 | 0.0016327 | 0.1893658 | 0.1957661 | 4.7656357 | 0.0000021 | 0.0362514 | 0.0008143 | 55 |
| light side distance travelled | Light-Dark Test | 111 | 84 | 6.4816547 | 0.1294897 | -2.5516943 | 1.7663288 | 6.3543715 | 0.1437888 | 3.6254523 | 1.8288414 | 0.1272832 | 0.1540843 | 0.4100225 | -6.1771466 | 2.4944392 | 0.0143335 | 0.9393093 | 0.9889885 | 0.1851685 | 0.4948512 | Behaviour | distance travelled | -0.0530811 | 0.0108025 | -0.0742535 | -0.0319086 | -0.5107144 | 0.6101347 | 0.1873295 | 0.0052083 | 56 |
| light side time spent | Light-Dark Test | 1844 | 1791 | 4.9654182 | 0.5269181 | -0.0021930 | 0.1579744 | 5.2239760 | 0.5269294 | -0.2118302 | 0.1342596 | -0.2585578 | 0.0196728 | 0.0000000 | 0.2096373 | 0.1833892 | 0.2530688 | 0.5977434 | 0.5803370 | 0.1214243 | 0.8423099 | Behaviour | time spent | 0.0295447 | 0.0005512 | 0.0284643 | 0.0306251 | 1.2583781 | 0.2083359 | 0.1220263 | 0.0002753 | 57 |
| light sleep bout lengths mean | Sleep Wake | 864 | 842 | 6.2807125 | 0.0075695 | 0.0529380 | 0.0732268 | 6.4177202 | 0.0079864 | -0.0992664 | 0.0683278 | -0.1370077 | 0.0086787 | 0.0000000 | 0.1522044 | 0.0940691 | 0.1058660 | 0.1716964 | 0.1848297 | 0.3447557 | 0.4550385 | Behaviour | light sleep bout lengths mean | -0.0737221 | 0.0011767 | -0.0760283 | -0.0714158 | -2.1491689 | 0.0317615 | 0.3594797 | 0.0005872 | 58 |
| light sleep bout lengths standard deviation | Sleep Wake | 864 | 842 | 6.6926176 | 0.0129927 | -0.1714906 | 0.1150069 | 6.7386821 | 0.0122796 | -0.4848465 | 0.0908174 | -0.0460646 | 0.0121790 | 0.0001613 | 0.3133558 | 0.1345985 | 0.0200369 | 0.2668549 | 0.2323942 | 0.1541689 | 0.4349426 | Behaviour | light sleep bout lengths standard deviation | 0.1382547 | 0.0011767 | 0.1359485 | 0.1405610 | 4.0304462 | 0.0000581 | 0.1554081 | 0.0005872 | 59 |
| locomotor activity | Combined SHIRPA and Dysmorphology | 9460 | 9489 | 3.0273932 | 0.0644435 | 0.1818249 | 0.0804029 | 2.9048847 | 0.0644800 | -0.0491620 | 0.0802510 | 0.1225086 | 0.0062172 | 0.0000000 | 0.2309870 | 0.0567145 | 0.0000467 | 0.3459570 | 0.3917692 | 0.1290016 | 0.6526259 | Behaviour | locomotor activity | -0.1243582 | 0.0001056 | -0.1245651 | -0.1241513 | -12.1027273 | 0.0000000 | 0.1297244 | 0.0000528 | 60 |
| ma threshold inducing clonic seizure | Electroconvulsive Threshold Testing | 788 | 750 | 1.7982152 | 0.0120420 | 0.1016569 | 0.0524737 | 1.9568640 | 0.0120144 | 0.2345712 | 0.0508352 | -0.1586488 | 0.0053063 | 0.0000000 | -0.1329143 | 0.0604580 | 0.0281056 | 0.0949636 | 0.0959180 | 0.4244441 | 0.8663846 | Behaviour | ma threshold inducing clonic seizure | -0.0100325 | 0.0013063 | -0.0125928 | -0.0074722 | -0.2775814 | 0.7813711 | 0.4531003 | 0.0006515 | 61 |
| peak wake with respect to dark onset median | Sleep Wake | 864 | 842 | 1.3203534 | 0.0198529 | 0.4477957 | 0.1803907 | 1.0185793 | 0.0238263 | -0.0215746 | 0.2031956 | 0.3017741 | 0.0237530 | 0.0000000 | 0.4693702 | 0.2537384 | 0.0645310 | 0.4081548 | 0.5555201 | 0.2851148 | 0.4328934 | Behaviour | peak wake with respect to dark onset median | -0.3082735 | 0.0011767 | -0.3105797 | -0.3059673 | -8.9868870 | 0.0000000 | 0.2932406 | 0.0005872 | 62 |
| percent time in dark | Light-Dark Test | 1844 | 1791 | 4.3786813 | 0.0105578 | 0.2184087 | 0.2457705 | 4.2700353 | 0.0134339 | 0.2190065 | 0.2538024 | 0.1086460 | 0.0134318 | 0.0000000 | -0.0005978 | 0.1194379 | 0.9960067 | 0.3210701 | 0.4720180 | 0.1431467 | 0.2261611 | Behaviour | percent time | -0.3853655 | 0.0005512 | -0.3864459 | -0.3842851 | -16.4136447 | 0.0000000 | 0.1441366 | 0.0002753 | 63 |
| percent time in light | Light-Dark Test | 1844 | 1791 | 2.4970200 | 0.2924894 | -0.4787242 | 0.6056567 | 2.8063942 | 0.2924091 | -0.5789618 | 0.5905545 | -0.3093742 | 0.0279962 | 0.0000000 | 0.1002375 | 0.2626685 | 0.7027736 | 0.8862887 | 0.7893137 | 0.1714170 | 0.5668412 | Behaviour | percent time | 0.1158709 | 0.0005512 | 0.1147905 | 0.1169513 | 4.9352197 | 0.0000008 | 0.1731261 | 0.0002753 | 64 |
| percentage center time | Open Field | 9048 | 8998 | 2.6098556 | 0.1510097 | -0.0910391 | 0.1293347 | 2.6423626 | 0.1510479 | -0.0221698 | 0.1225198 | -0.0325069 | 0.0101763 | 0.0014040 | -0.0688694 | 0.0936403 | 0.4620659 | 0.6488230 | 0.5855666 | 0.0198205 | 0.7130754 | Behaviour | percentage center time | 0.1025798 | 0.0001109 | 0.1023625 | 0.1027971 | 9.7423426 | 0.0000000 | 0.0198230 | 0.0000554 | 65 |
| periphery average speed | Open Field | 8693 | 8662 | 2.0049452 | 0.2238235 | -0.0829681 | 0.0618082 | 1.9012928 | 0.2238249 | -0.0452305 | 0.0609968 | 0.1036524 | 0.0031592 | 0.0000000 | -0.0377375 | 0.0290595 | 0.1940907 | 0.1949375 | 0.1843252 | 0.0651890 | 0.9715487 | Behaviour | periphery average speed | 0.0559774 | 0.0001153 | 0.0557514 | 0.0562033 | 5.2135562 | 0.0000002 | 0.0652815 | 0.0000576 | 66 |
| periphery distance travelled | Open Field | 9026 | 8992 | 8.5874128 | 0.2033425 | -0.0829594 | 0.0986697 | 8.4801189 | 0.2033441 | -0.0201656 | 0.0983814 | 0.1072939 | 0.0035387 | 0.0000000 | -0.0627938 | 0.0328350 | 0.0558426 | 0.2170241 | 0.2166794 | 0.0725156 | 0.9566476 | Behaviour | periphery distance travelled | 0.0015894 | 0.0001110 | 0.0013718 | 0.0018070 | 0.1508324 | 0.8801096 | 0.0726431 | 0.0000555 | 67 |
| periphery permanence time | Open Field | 9382 | 9328 | 6.8707925 | 0.0282661 | -0.0258067 | 0.0234157 | 6.8740175 | 0.0282822 | 0.0246626 | 0.0230624 | -0.0032251 | 0.0018507 | 0.0814181 | -0.0504693 | 0.0170113 | 0.0030135 | 0.1100146 | 0.1164500 | 0.0183902 | 0.7390468 | Behaviour | periphery time | -0.0568491 | 0.0001069 | -0.0570587 | -0.0566396 | -5.4976145 | 0.0000000 | 0.0183923 | 0.0000535 | 68 |
| periphery resting time | Open Field | 6472 | 6443 | 5.7710269 | 0.2357515 | -0.2289353 | 0.1265413 | 5.7521748 | 0.2357413 | -0.0623412 | 0.1266620 | 0.0188517 | 0.0051703 | 0.0002674 | -0.1665938 | 0.0545213 | 0.0022515 | 0.2493776 | 0.2618262 | 0.0221010 | 0.9485414 | Behaviour | periphery time | -0.0487132 | 0.0001549 | -0.0490169 | -0.0484095 | -3.9136034 | 0.0000914 | 0.0221046 | 0.0000774 | 69 |
| repetitive beam break (‘stereotypy counts’) | Light-Dark Test | 111 | 84 | 5.5952858 | 0.0411741 | 0.5236469 | 0.6755584 | 5.8007827 | 0.0396023 | -0.6213184 | 0.5483999 | -0.2054969 | 0.0518326 | 0.0001114 | 1.1449653 | 0.8607966 | 0.1854068 | 0.3798610 | 0.3100351 | 0.2835955 | 0.3913126 | Behaviour | repetitive beam break (‘stereotypy counts’) | 0.2015766 | 0.0108025 | 0.1804041 | 0.2227490 | 1.9394498 | 0.0539044 | 0.2915877 | 0.0052083 | 70 |
| side changes | Light-Dark Test | 1844 | 1791 | 3.5274996 | 0.4881902 | 0.8546110 | 0.2143882 | 3.4650413 | 0.4881913 | 0.0001478 | 0.2001931 | 0.0624583 | 0.0179176 | 0.0004970 | 0.8544632 | 0.1670303 | 0.0000003 | 0.5526041 | 0.5216740 | 0.0611345 | 0.8471911 | Behaviour | side changes | 0.0575910 | 0.0005512 | 0.0565106 | 0.0586714 | 2.4529393 | 0.0142161 | 0.0612108 | 0.0002753 | 71 |
| sleep bout lengths mean | Sleep Wake | 864 | 842 | 5.8955216 | 0.0067615 | 0.1933935 | 0.0693342 | 6.0830951 | 0.0074012 | 0.1919395 | 0.0671167 | -0.1875736 | 0.0085770 | 0.0000000 | 0.0014541 | 0.0922525 | 0.9874263 | 0.1663868 | 0.1859924 | 0.4645746 | 0.5162762 | Behaviour | sleep bout lengths mean | -0.1114062 | 0.0011767 | -0.1137124 | -0.1090999 | -3.2477478 | 0.0011858 | 0.5031293 | 0.0005872 | 72 |
| sleep bout lengths standard deviation | Sleep Wake | 864 | 842 | 6.4469419 | 0.0113944 | -0.0915931 | 0.1033550 | 6.5079975 | 0.0108285 | -0.3782798 | 0.0827469 | -0.0610557 | 0.0110796 | 0.0000000 | 0.2866868 | 0.1221710 | 0.0190716 | 0.2407255 | 0.2138523 | 0.1637244 | 0.4172816 | Behaviour | sleep bout lengths standard deviation | 0.1183565 | 0.0011767 | 0.1160503 | 0.1206627 | 3.4503664 | 0.0005735 | 0.1652113 | 0.0005872 | 73 |
| sleep daily percent | Sleep Wake | 864 | 842 | 3.7371581 | 0.0041784 | -0.0701036 | 0.0383834 | 3.8084430 | 0.0038740 | 0.0774828 | 0.0295862 | -0.0712848 | 0.0040551 | 0.0000000 | -0.1475865 | 0.0448234 | 0.0010151 | 0.0899794 | 0.0761820 | 0.3731787 | 0.5146524 | Behaviour | sleep daily percent | 0.1664406 | 0.0011767 | 0.1641343 | 0.1687468 | 4.8521280 | 0.0000013 | 0.3921110 | 0.0005872 | 74 |
| sleep dark phase percent | Sleep Wake | 864 | 842 | 3.0450431 | 0.0109201 | -0.1041037 | 0.1093795 | 3.2752198 | 0.0094903 | 0.4542353 | 0.0787006 | -0.2301768 | 0.0115132 | 0.0000000 | -0.5583391 | 0.1271995 | 0.0000121 | 0.2635742 | 0.2073675 | 0.4335350 | 0.5099030 | Behaviour | sleep phase percent | 0.2398268 | 0.0011767 | 0.2375206 | 0.2421330 | 6.9915067 | 0.0000000 | 0.4642416 | 0.0005872 | 75 |
| sleep light phase percent | Sleep Wake | 864 | 842 | 4.1328580 | 0.0037644 | -0.0652945 | 0.0324612 | 4.1465681 | 0.0035095 | -0.0724303 | 0.0248494 | -0.0137100 | 0.0033710 | 0.0000500 | 0.0071359 | 0.0373876 | 0.8486586 | 0.0752252 | 0.0627068 | 0.1227691 | 0.4475686 | Behaviour | sleep phase percent | 0.1820011 | 0.0011767 | 0.1796949 | 0.1843074 | 5.3057549 | 0.0000001 | 0.1233916 | 0.0005872 | 76 |
| time immobile | Tail Suspension | 586 | 585 | 4.8771036 | 0.0532482 | -0.1171893 | 0.2946482 | 4.9068450 | 0.0537955 | -0.2326952 | 0.2988787 | -0.0297414 | 0.0181467 | 0.1015187 | 0.1155060 | 0.1804693 | 0.5222887 | 0.2877956 | 0.3297647 | 0.0636955 | 0.5995012 | Behaviour | time immobile | -0.1361305 | 0.0017167 | -0.1394953 | -0.1327658 | -3.2855139 | 0.0010482 | 0.0637818 | 0.0008562 | 77 |
| time mobile dark side | Light-Dark Test | 1844 | 1791 | 5.2447560 | 0.1909156 | 0.3956414 | 0.2175214 | 5.0469563 | 0.1911008 | 0.1000626 | 0.2230781 | 0.1977997 | 0.0142692 | 0.0000000 | 0.2955788 | 0.1288735 | 0.0218787 | 0.3555980 | 0.4887962 | 0.1844971 | 0.6537158 | Behaviour | time mobile | -0.3181527 | 0.0005512 | -0.3192331 | -0.3170723 | -13.5508903 | 0.0000000 | 0.1866343 | 0.0002753 | 78 |
| time mobile light side | Light-Dark Test | 1844 | 1791 | 3.9968290 | 0.2059431 | 0.4383632 | 0.1674147 | 4.0861149 | 0.2059514 | -0.1933410 | 0.1457041 | -0.0892859 | 0.0187350 | 0.0000020 | 0.6317042 | 0.1751338 | 0.0003144 | 0.5747938 | 0.5460658 | 0.0826495 | 0.5835975 | Behaviour | time mobile | 0.0512639 | 0.0005512 | 0.0501835 | 0.0523443 | 2.1834525 | 0.0290663 | 0.0828384 | 0.0002753 | 79 |
| total distance travelled | Light-Dark Test | 111 | 84 | 8.3486951 | 0.0424659 | -0.3966535 | 0.5475750 | 8.3103678 | 0.0447656 | 1.1429387 | 0.5327208 | 0.0383273 | 0.0460295 | 0.4062972 | -1.5395922 | 0.7473338 | 0.0410384 | 0.2895251 | 0.2785499 | 0.1664589 | 0.5378201 | Behaviour | total distance travelled | 0.0371015 | 0.0108025 | 0.0159290 | 0.0582739 | 0.3569682 | 0.7215056 | 0.1680224 | 0.0052083 | 80 |
| total holepokes | Hole-board Exploration | 1357 | 1324 | 3.6303405 | 0.0122218 | 0.3670670 | 0.1003758 | 3.5870204 | 0.0133169 | 0.3310115 | 0.0964953 | 0.0433201 | 0.0133826 | 0.0012238 | 0.0360555 | 0.1243962 | 0.7719602 | 0.3194311 | 0.3683197 | 0.1195821 | 0.3663792 | Behaviour | total holepokes | -0.1424190 | 0.0007478 | -0.1438846 | -0.1409534 | -5.2081304 | 0.0000002 | 0.1201571 | 0.0003734 | 81 |
| vertical activity (rearing) | Light-Dark Test | 111 | 84 | 4.1085951 | 0.0844723 | 1.4389900 | 1.2587160 | 4.1897357 | 0.0971752 | 1.0980580 | 1.3123950 | -0.0811407 | 0.1104630 | 0.4637090 | 0.3409321 | 1.7912888 | 0.8492981 | 0.6829218 | 0.7340029 | 0.1151428 | 0.3962937 | Behaviour | vertical activity (rearing) | -0.0736758 | 0.0108025 | -0.0948482 | -0.0525033 | -0.7088644 | 0.4792640 | 0.1156558 | 0.0052083 | 82 |
| whole arena average speed | Open Field | 9384 | 9328 | 2.1833594 | 0.1900451 | -0.0666238 | 0.0879635 | 2.0847851 | 0.1900420 | -0.0221142 | 0.0874200 | 0.0985743 | 0.0030312 | 0.0000000 | -0.0445096 | 0.0279880 | 0.1117844 | 0.1909462 | 0.1807443 | 0.0685855 | 0.9662303 | Behaviour | whole arena average speed | 0.0549077 | 0.0001069 | 0.0546981 | 0.0551172 | 5.3101472 | 0.0000001 | 0.0686934 | 0.0000535 | 83 |
| whole arena permanence | Open Field | 9051 | 8998 | 7.0900506 | 0.0000240 | -0.0000287 | 0.0000645 | 7.0900531 | 0.0000241 | -0.0000565 | 0.0000645 | -0.0000025 | 0.0000063 | 0.6916046 | 0.0000278 | 0.0000559 | 0.6194567 | 0.0003329 | 0.0003913 | 0.0113258 | 0.6312850 | Behaviour | whole arena | -0.1614523 | 0.0001108 | -0.1616696 | -0.1612351 | -15.3349311 | 0.0000000 | 0.0113263 | 0.0000554 | 84 |
| whole arena resting time | Open Field | 9379 | 9323 | 5.7783462 | 0.2368967 | -0.0194123 | 0.1107347 | 5.7747759 | 0.2368936 | -0.1052451 | 0.1101210 | 0.0035702 | 0.0035891 | 0.3198782 | 0.0858328 | 0.0329694 | 0.0092385 | 0.2250023 | 0.2092492 | 0.0089450 | 0.9710223 | Behaviour | whole arena | 0.0725846 | 0.0001070 | 0.0723750 | 0.0727943 | 7.0178193 | 0.0000000 | 0.0089452 | 0.0000535 | 85 |
| cone b-wave amplitude | Electroretinography | 109 | 106 | 4.7870215 | 0.0203566 | -0.2653534 | 0.2866064 | 4.7609002 | 0.0227586 | -0.3036349 | 0.2792957 | 0.0261213 | 0.0278571 | 0.3500547 | 0.0382815 | 0.3983396 | 0.9235795 | 0.1826788 | 0.2073646 | 0.1126656 | 0.3950130 | Eye | cone b-wave amplitude | -0.1268861 | 0.0095714 | -0.1456456 | -0.1081265 | -1.2969621 | 0.1960474 | 0.1131460 | 0.0047170 | 86 |
| cone b-wave amplitude-left | Electroretinography 2 | 88 | 90 | 4.7492528 | 0.0530509 | -0.1125068 | 0.5717275 | 4.7557599 | 0.0499589 | 0.0072912 | 0.5143473 | -0.0065071 | 0.0304633 | 0.8312030 | -0.1197980 | 0.3403797 | 0.7254655 | 0.2341936 | 0.1656133 | 0.0352267 | 0.2741236 | Eye | cone b-wave amplitude | 0.3466279 | 0.0116295 | 0.3238346 | 0.3694213 | 3.2142778 | 0.0015558 | 0.0352413 | 0.0057143 | 87 |
| cone b-wave amplitude-right | Electroretinography 2 | 88 | 90 | 4.6920186 | 0.0328763 | -0.1092556 | 0.3510624 | 4.6619672 | 0.0351153 | -0.2944697 | 0.3492731 | 0.0300513 | 0.0246392 | 0.2248911 | 0.1852141 | 0.2614969 | 0.4800876 | 0.1410758 | 0.1846999 | 0.1637512 | 0.3745252 | Eye | cone b-wave amplitude | -0.2693002 | 0.0116295 | -0.2920935 | -0.2465068 | -2.4972181 | 0.0134356 | 0.1652388 | 0.0057143 | 88 |
| cone b-wave implicit time | Electroretinography | 109 | 106 | 3.8065135 | 0.0072872 | 0.0130828 | 0.0888663 | 3.7975142 | 0.0067864 | -0.0204553 | 0.0683745 | 0.0089992 | 0.0073778 | 0.2246499 | 0.0335382 | 0.1115520 | 0.7641366 | 0.0535270 | 0.0440410 | 0.0742520 | 0.6683812 | Eye | cone b-wave implicit time | 0.1949282 | 0.0095714 | 0.1761686 | 0.2136877 | 1.9924524 | 0.0475982 | 0.0743889 | 0.0047170 | 89 |
| cone b-wave implicit time-left | Electroretinography 2 | 88 | 90 | 3.6935272 | 0.0078872 | 0.0523171 | 0.0830820 | 3.6813641 | 0.0075282 | -0.0681515 | 0.0748627 | 0.0121631 | 0.0044453 | 0.0071213 | 0.1204686 | 0.0508235 | 0.0193013 | 0.0332622 | 0.0252082 | 0.2459276 | 0.5294324 | Eye | cone b-wave implicit time | 0.2773872 | 0.0116295 | 0.2545939 | 0.3001806 | 2.5722093 | 0.0109306 | 0.2510736 | 0.0057143 | 90 |
| cone b-wave implicit time-right | Electroretinography 2 | 88 | 90 | 3.6948828 | 0.0072891 | 0.0557507 | 0.0763886 | 3.6762197 | 0.0070888 | -0.0337672 | 0.0698221 | 0.0186631 | 0.0042795 | 0.0000268 | 0.0895179 | 0.0484346 | 0.0669334 | 0.0304230 | 0.0262671 | 0.2935941 | 0.5841044 | Eye | cone b-wave implicit time | 0.1470169 | 0.0116295 | 0.1242235 | 0.1698103 | 1.3632866 | 0.1745336 | 0.3024948 | 0.0057143 | 91 |
| eye size | Electroretinography | 109 | 106 | 1.1670972 | 0.0052240 | 0.0458615 | 0.0641530 | 1.1809462 | 0.0045491 | -0.0461844 | 0.0440353 | -0.0138490 | 0.0050830 | 0.0072780 | 0.0920459 | 0.0775401 | 0.2372522 | 0.0392531 | 0.0271982 | 0.1768871 | 0.6729457 | Eye | eye size | 0.3667432 | 0.0095714 | 0.3479837 | 0.3855027 | 3.7486546 | 0.0002290 | 0.1787674 | 0.0047170 | 92 |
| eye size-left | Electroretinography 2 | 88 | 90 | 1.1704321 | 0.0040562 | 0.0327395 | 0.0432604 | 1.1803699 | 0.0039531 | -0.0009597 | 0.0397741 | -0.0099378 | 0.0024713 | 0.0000995 | 0.0336992 | 0.0275744 | 0.2239613 | 0.0174252 | 0.0153890 | 0.2838146 | 0.4725799 | Eye | eye size | 0.1244045 | 0.0116295 | 0.1016112 | 0.1471979 | 1.1536020 | 0.2502276 | 0.2918260 | 0.0057143 | 93 |
| eye size-right | Electroretinography 2 | 88 | 90 | 1.1696790 | 0.0028662 | 0.0280498 | 0.0308803 | 1.1772792 | 0.0029487 | -0.0136714 | 0.0299142 | -0.0076002 | 0.0020274 | 0.0002704 | 0.0417211 | 0.0214992 | 0.0545592 | 0.0126370 | 0.0143716 | 0.2898198 | 0.4129712 | Eye | eye size | -0.1284857 | 0.0116295 | -0.1512791 | -0.1056923 | -1.1914467 | 0.2350825 | 0.2983695 | 0.0057143 | 94 |
| interpupillary distance | Electroretinography | 197 | 196 | 2.4364589 | 0.0085984 | 0.0166388 | 0.0257003 | 2.4522604 | 0.0085764 | 0.0313432 | 0.0215600 | -0.0158015 | 0.0021803 | 0.0000000 | -0.0147044 | 0.0265916 | 0.5807489 | 0.0220370 | 0.0202813 | 0.3075024 | 0.6117103 | Eye | interpupillary distance | 0.0830107 | 0.0051680 | 0.0728816 | 0.0931398 | 1.1547105 | 0.2489142 | 0.3177846 | 0.0025641 | 95 |
| left anterior chamber depth | Eye Morphology | 76 | 77 | 5.9022668 | 0.0129129 | 0.3309173 | 0.1731418 | 5.9362184 | 0.0121212 | 0.0545907 | 0.1491186 | -0.0339516 | 0.0080803 | 0.0000960 | 0.2763266 | 0.1151950 | 0.0198049 | 0.0506303 | 0.0341372 | 0.3794858 | 0.6994703 | Eye | left anterior chamber depth | 0.3942562 | 0.0136061 | 0.3675888 | 0.4209236 | 3.3799647 | 0.0009228 | 0.3994588 | 0.0066667 | 96 |
| left corneal thickness | Eye Morphology | 76 | 77 | 4.6124114 | 0.0249485 | 0.2367440 | 0.3349191 | 4.6009145 | 0.0228457 | 0.0955969 | 0.2845949 | 0.0114969 | 0.0142320 | 0.4226134 | 0.1411472 | 0.2086622 | 0.5015467 | 0.0993737 | 0.0497623 | 0.1212388 | 0.4994023 | Eye | corneal thickness | 0.6917221 | 0.0136061 | 0.6650547 | 0.7183895 | 5.9301444 | 0.0000000 | 0.1218381 | 0.0066667 | 97 |
| left inner nuclear layer | Eye Morphology | 75 | 77 | 3.1647719 | 0.0355175 | 0.2654699 | 0.4620566 | 3.1572765 | 0.0360083 | 0.0064017 | 0.4487509 | 0.0074954 | 0.0175264 | 0.6705372 | 0.2590682 | 0.2323272 | 0.2695700 | 0.0930703 | 0.1092257 | 0.1081321 | 0.5139294 | Eye | inner nuclear layer | -0.1598729 | 0.0137012 | -0.1867267 | -0.1330190 | -1.3658264 | 0.1740379 | 0.1085566 | 0.0067114 | 98 |
| left outer nuclear layer | Eye Morphology | 75 | 77 | 3.7882792 | 0.0348459 | -0.3177201 | 0.2634062 | 3.7895956 | 0.0345014 | 0.1101670 | 0.2242820 | -0.0013165 | 0.0150869 | 0.9307775 | -0.4278871 | 0.2051849 | 0.0416052 | 0.0580967 | 0.0493776 | 0.0566948 | 0.9830572 | Eye | outer nuclear layer | 0.1627997 | 0.0137012 | 0.1359458 | 0.1896535 | 1.3908306 | 0.1663366 | 0.0567557 | 0.0067114 | 99 |
| left posterior chamber depth | Eye Morphology | 75 | 77 | 6.2950004 | 0.0057413 | 0.0134718 | 0.0721440 | 6.2932623 | 0.0052378 | 0.0000086 | 0.0510594 | 0.0017381 | 0.0047278 | 0.7145278 | 0.0134632 | 0.0663471 | 0.8399331 | 0.0244016 | 0.0145372 | 0.0275267 | 0.8827247 | Eye | posterior chamber depth | 0.5181289 | 0.0137012 | 0.4912751 | 0.5449828 | 4.4264800 | 0.0000183 | 0.0275336 | 0.0067114 | 100 |
| left total retinal thickness | Eye Morphology | 1222 | 1261 | 5.4658882 | 0.0421549 | 0.0206518 | 0.0154353 | 5.4677307 | 0.0421457 | -0.0073512 | 0.0087732 | -0.0018425 | 0.0014312 | 0.1981084 | 0.0280030 | 0.0170013 | 0.0996828 | 0.0403168 | 0.0259744 | 0.0187548 | 0.9126391 | Eye | total retinal thickness | 0.4396691 | 0.0008076 | 0.4380862 | 0.4412521 | 15.4710630 | 0.0000000 | 0.0187570 | 0.0004032 | 101 |
| max left eye lens density | Eye Morphology | 907 | 942 | 2.1608371 | 0.0173110 | 0.1228336 | 0.1551037 | 2.1471969 | 0.0169681 | 0.2246610 | 0.1392837 | 0.0136401 | 0.0096236 | 0.1565651 | -0.1018274 | 0.1119263 | 0.3630765 | 0.2068967 | 0.1819754 | 0.0776843 | 0.6307797 | Eye | max eye lens density | 0.1283686 | 0.0010856 | 0.1262409 | 0.1304963 | 3.8960820 | 0.0001012 | 0.0778411 | 0.0005417 | 102 |
| max right eye lens density | Eye Morphology | 896 | 940 | 2.1891450 | 0.0183517 | 0.1189974 | 0.1653545 | 2.1854203 | 0.0180324 | 0.2512921 | 0.1495362 | 0.0037248 | 0.0101963 | 0.7149312 | -0.1322947 | 0.1183383 | 0.2637597 | 0.2161151 | 0.1947696 | 0.0758212 | 0.6280351 | Eye | max eye lens density | 0.1040203 | 0.0010935 | 0.1018770 | 0.1061635 | 3.1455956 | 0.0016841 | 0.0759670 | 0.0005456 | 103 |
| mean left eye lens density | Eye Morphology | 907 | 942 | 1.8486306 | 0.0126604 | 0.1268596 | 0.1054418 | 1.8557029 | 0.0125326 | 0.2173133 | 0.0964873 | -0.0070722 | 0.0064691 | 0.2744529 | -0.0904537 | 0.0747436 | 0.2263820 | 0.1348207 | 0.1259803 | 0.0989529 | 0.6982171 | Eye | mean eye lens density | 0.0678410 | 0.0010856 | 0.0657133 | 0.0699687 | 2.0590251 | 0.0396318 | 0.0992778 | 0.0005417 | 104 |
| mean right eye lens density | Eye Morphology | 896 | 940 | 1.8964623 | 0.0132411 | 0.1548917 | 0.1040921 | 1.9004944 | 0.0131912 | 0.2231561 | 0.0968683 | -0.0040321 | 0.0063068 | 0.5226990 | -0.0682645 | 0.0724011 | 0.3458903 | 0.1269518 | 0.1260334 | 0.1001870 | 0.7412178 | Eye | mean eye lens density | 0.0072869 | 0.0010935 | 0.0051436 | 0.0094302 | 0.2203577 | 0.8256171 | 0.1005243 | 0.0005456 | 105 |
| min left eye lens density | Eye Morphology | 907 | 942 | 1.5772555 | 0.0088661 | 0.0319590 | 0.0806445 | 1.5768524 | 0.0087628 | 0.1064950 | 0.0738049 | 0.0004031 | 0.0049339 | 0.9348960 | -0.0745360 | 0.0569851 | 0.1910599 | 0.1032527 | 0.0966649 | 0.0639918 | 0.6167108 | Eye | min eye lens density | 0.0659496 | 0.0010856 | 0.0638219 | 0.0680773 | 2.0016184 | 0.0454718 | 0.0640793 | 0.0005417 | 106 |
| min right eye lens density | Eye Morphology | 896 | 940 | 1.6231681 | 0.0094026 | 0.0258442 | 0.0822394 | 1.6282361 | 0.0092900 | 0.0899020 | 0.0751669 | -0.0050680 | 0.0050377 | 0.3145526 | -0.0640578 | 0.0582431 | 0.2715669 | 0.1049264 | 0.0978915 | 0.0550421 | 0.6550367 | Eye | min eye lens density | 0.0694252 | 0.0010935 | 0.0672820 | 0.0715685 | 2.0994349 | 0.0359149 | 0.0550978 | 0.0005456 | 107 |
| right anterior chamber depth | Eye Morphology | 74 | 76 | 5.8499653 | 0.0744481 | 0.3513655 | 1.0024399 | 5.9094482 | 0.0658886 | 0.0062495 | 0.8386929 | -0.0594829 | 0.0356560 | 0.1009511 | 0.3451160 | 0.5568692 | 0.5379860 | 0.3012121 | 0.0445532 | 0.1516148 | 0.3280283 | Eye | anterior chamber depth | 1.9113246 | 0.0138916 | 1.8840976 | 1.9385515 | 16.2165624 | 0.0000000 | 0.1527928 | 0.0068027 | 108 |
| right corneal thickness | Eye Morphology | 75 | 76 | 4.5849567 | 0.0240284 | 0.0817905 | 0.3172213 | 4.6190344 | 0.0371159 | -0.0386156 | 0.4210329 | -0.0340778 | 0.0343022 | 0.3247599 | 0.1204061 | 0.4051953 | 0.7674471 | 0.0853730 | 0.2640478 | 0.0876635 | 0.4151909 | Eye | corneal thickness | -1.1290047 | 0.0137938 | -1.1560399 | -1.1019694 | -9.6128997 | 0.0000000 | 0.0878891 | 0.0067568 | 109 |
| right inner nuclear layer | Eye Morphology | 71 | 75 | 3.1827405 | 0.0329055 | 0.0228435 | 0.4349801 | 3.1421454 | 0.0316025 | -0.0938985 | 0.3884290 | 0.0405951 | 0.0194194 | 0.0413907 | 0.1167420 | 0.2714789 | 0.6689228 | 0.1165745 | 0.0924917 | 0.1657853 | 0.6499986 | Eye | inner nuclear layer | 0.2318196 | 0.0142974 | 0.2037973 | 0.2598420 | 1.9387502 | 0.0544875 | 0.1673297 | 0.0069930 | 110 |
| right outer nuclear layer | Eye Morphology | 71 | 75 | 3.8142594 | 0.0363759 | 0.2119433 | 0.3111399 | 3.7988066 | 0.0362905 | 0.1023941 | 0.2907425 | 0.0154528 | 0.0166967 | 0.3588998 | 0.1095492 | 0.2146274 | 0.6118796 | 0.0501008 | 0.0647724 | 0.0513844 | 0.9781193 | Eye | outer nuclear layer | -0.2564333 | 0.0142974 | -0.2844557 | -0.2284110 | -2.1445994 | 0.0336631 | 0.0514297 | 0.0069930 | 111 |
| right posterior chamber depth | Eye Morphology | 72 | 75 | 6.2934395 | 0.0069264 | -0.0450790 | 0.0914310 | 6.2917623 | 0.0068354 | -0.0476841 | 0.0804849 | 0.0016772 | 0.0051949 | 0.7480804 | 0.0026050 | 0.0703540 | 0.9706022 | 0.0252662 | 0.0250891 | 0.1105265 | 0.6829920 | Eye | posterior chamber depth | 0.0073356 | 0.0141908 | -0.0204779 | 0.0351491 | 0.0615787 | 0.9509831 | 0.1109799 | 0.0069444 | 112 |
| right total retinal thickness | Eye Morphology | 1200 | 1250 | 5.4713411 | 0.0401823 | 0.0198160 | 0.0199044 | 5.4722737 | 0.0401777 | 0.0067269 | 0.0170250 | -0.0009325 | 0.0013988 | 0.5050496 | 0.0130891 | 0.0163345 | 0.4230406 | 0.0360767 | 0.0296700 | 0.0163814 | 0.9095386 | Eye | total retinal thickness | 0.1955281 | 0.0008187 | 0.1939235 | 0.1971327 | 6.8336676 | 0.0000000 | 0.0163829 | 0.0004087 | 113 |
| rod a-wave amplitude | Electroretinography | 108 | 106 | 5.0019222 | 0.0377832 | 0.2899168 | 0.5022084 | 4.6538302 | 0.0581622 | 1.8001844 | 0.7181090 | 0.3480920 | 0.0630488 | 0.0000002 | -1.5102676 | 0.8717296 | 0.0854524 | 0.3040800 | 0.5446975 | 0.3909238 | 0.5887973 | Eye | rod a-wave amplitude | -0.5830322 | 0.0096163 | -0.6018798 | -0.5641847 | -5.9455105 | 0.0000000 | 0.4128900 | 0.0047393 | 114 |
| rod a-wave amplitude-left | Electroretinography 2 | 88 | 89 | 4.4019232 | 0.1851483 | -1.6951873 | 1.9890855 | 4.4685010 | 0.1664608 | 0.1811786 | 1.7195832 | -0.0665778 | 0.0914556 | 0.4679986 | -1.8763658 | 1.0778893 | 0.0842030 | 0.8100048 | 0.2819804 | 0.1829123 | 0.3245975 | Eye | rod a-wave amplitude | 1.0552711 | 0.0116963 | 1.0323468 | 1.0781954 | 9.7575250 | 0.0000000 | 0.1849941 | 0.0057471 | 115 |
| rod a-wave implicit time | Electroretinography | 109 | 106 | 2.8729591 | 0.0069714 | 0.0641289 | 0.1020216 | 2.8093780 | 0.0064500 | -0.0172914 | 0.0802644 | 0.0635812 | 0.0089484 | 0.0000000 | 0.0814202 | 0.1293893 | 0.5302232 | 0.0671268 | 0.0600533 | 0.4326568 | 0.5087558 | Eye | rod a-wave implicit time | 0.1112130 | 0.0095714 | 0.0924535 | 0.1299725 | 1.1367605 | 0.2569157 | 0.4631608 | 0.0047170 | 116 |
| rod a-wave implicit time-left | Electroretinography 2 | 86 | 90 | 3.0911387 | 0.0121993 | -0.0488955 | 0.1302484 | 3.0354671 | 0.0119366 | -0.0835053 | 0.1201750 | 0.0556715 | 0.0075779 | 0.0000000 | 0.0346098 | 0.0842643 | 0.6819858 | 0.0521901 | 0.0478881 | 0.4656808 | 0.5839650 | Eye | rod a-wave implicit time | 0.0863021 | 0.0117712 | 0.0632309 | 0.1093732 | 0.7954454 | 0.4274382 | 0.5045408 | 0.0057803 | 117 |
| rod a-wave implicit time-right | Electroretinography 2 | 87 | 90 | 3.0952411 | 0.0118748 | 0.0035083 | 0.1269810 | 3.0375437 | 0.0118543 | -0.0615563 | 0.1195903 | 0.0576974 | 0.0077889 | 0.0000000 | 0.0650646 | 0.0845818 | 0.4432072 | 0.0514602 | 0.0520203 | 0.4682790 | 0.5622055 | Eye | rod a-wave implicit time | -0.0106195 | 0.0116995 | -0.0335501 | 0.0123111 | -0.0981794 | 0.9219022 | 0.5078637 | 0.0057471 | 118 |
| rod b-wave amplitude | Electroretinography | 109 | 106 | 6.0257997 | 0.0266942 | -0.3259945 | 0.3348736 | 6.1649995 | 0.0289647 | -0.3521175 | 0.3232280 | -0.1391998 | 0.0315693 | 0.0000208 | 0.0261230 | 0.4622022 | 0.9550110 | 0.1992254 | 0.2260429 | 0.2762841 | 0.6140759 | Eye | rod b-wave amplitude | -0.1264254 | 0.0095714 | -0.1451849 | -0.1076659 | -1.2922531 | 0.1976697 | 0.2836545 | 0.0047170 | 119 |
| rod b-wave amplitude-left | Electroretinography 2 | 88 | 90 | 6.0308212 | 0.0532423 | -0.2354132 | 0.5674087 | 6.0745898 | 0.0505979 | -0.1624100 | 0.5108783 | -0.0437686 | 0.0303432 | 0.1516757 | -0.0730032 | 0.3448839 | 0.8327056 | 0.2288448 | 0.1700985 | 0.1262252 | 0.4502565 | Eye | rod b-wave amplitude | 0.2968014 | 0.0116295 | 0.2740081 | 0.3195948 | 2.7522370 | 0.0065392 | 0.1269020 | 0.0057143 | 120 |
| rod b-wave amplitude-right | Electroretinography 2 | 88 | 90 | 6.0279696 | 0.0352112 | -0.3128628 | 0.3794693 | 6.0481981 | 0.0355755 | -0.3156275 | 0.3621245 | -0.0202285 | 0.0239775 | 0.4004793 | 0.0027646 | 0.2561827 | 0.9914069 | 0.1554400 | 0.1644146 | 0.1885856 | 0.3269960 | Eye | rod b-wave amplitude | -0.0559960 | 0.0116295 | -0.0787894 | -0.0332026 | -0.5192505 | 0.6042384 | 0.1908702 | 0.0057143 | 121 |
| rod b-wave implicit time | Electroretinography | 109 | 106 | 3.8244624 | 0.0092076 | -0.0901275 | 0.1075874 | 3.7998822 | 0.0089473 | -0.1289518 | 0.0881847 | 0.0245802 | 0.0091505 | 0.0081209 | 0.0388243 | 0.1382850 | 0.7793213 | 0.0634114 | 0.0571168 | 0.1778487 | 0.6874958 | Eye | rod b-wave implicit time | 0.1044079 | 0.0095714 | 0.0856484 | 0.1231674 | 1.0672022 | 0.2870886 | 0.1797602 | 0.0047170 | 122 |
| rod b-wave implicit time-left | Electroretinography 2 | 87 | 90 | 3.9500425 | 0.0099464 | 0.0213216 | 0.1063083 | 3.9208392 | 0.0097487 | -0.0557267 | 0.0985488 | 0.0292033 | 0.0062410 | 0.0000074 | 0.0770483 | 0.0685730 | 0.2633556 | 0.0430703 | 0.0396997 | 0.3258317 | 0.4747225 | Eye | rod b-wave implicit time | 0.0816967 | 0.0116995 | 0.0587660 | 0.1046273 | 0.7553018 | 0.4510833 | 0.3381577 | 0.0057471 | 123 |
| rod b-wave implicit time-right | Electroretinography 2 | 87 | 90 | 3.9420083 | 0.0155275 | -0.1307904 | 0.1667339 | 3.9198784 | 0.0146860 | -0.0316754 | 0.1505488 | 0.0221298 | 0.0090282 | 0.0156316 | -0.0991150 | 0.1003707 | 0.3253259 | 0.0681811 | 0.0502322 | 0.2144275 | 0.3710464 | Eye | rod b-wave implicit time | 0.3057152 | 0.0116995 | 0.2827846 | 0.3286458 | 2.8263975 | 0.0052555 | 0.2178077 | 0.0057471 | 124 |
| % pre-pulse inhibition - global | Acoustic Startle and Pre-pulse Inhibition (PPI) | 8612 | 8634 | 5.2776037 | 0.0146528 | -0.0323998 | 0.0182306 | 5.2780673 | 0.0146456 | -0.0154401 | 0.0169923 | -0.0004636 | 0.0015264 | 0.7613324 | -0.0169597 | 0.0151777 | 0.2638358 | 0.0987059 | 0.0899306 | 0.0212099 | 0.5781311 | Hearing | pre-pulse inhibition | 0.0931073 | 0.0001160 | 0.0928799 | 0.0933347 | 8.6444435 | 0.0000000 | 0.0212131 | 0.0000580 | 125 |
| % pre-pulse inhibition - ppi1 | Acoustic Startle and Pre-pulse Inhibition (PPI) | 8611 | 8635 | 5.9221175 | 0.0103094 | -0.0079581 | 0.0131617 | 5.9189087 | 0.0103201 | -0.0051417 | 0.0135103 | 0.0032088 | 0.0011570 | 0.0055563 | -0.0028164 | 0.0114259 | 0.8053064 | 0.0652979 | 0.0792964 | 0.0200865 | 0.5396636 | Hearing | pre-pulse inhibition | -0.1942318 | 0.0001160 | -0.1944591 | -0.1940044 | -18.0332268 | 0.0000000 | 0.0200892 | 0.0000580 | 126 |
| % pre-pulse inhibition - ppi2 | Acoustic Startle and Pre-pulse Inhibition (PPI) | 8612 | 8634 | 5.5371597 | 0.0100693 | -0.0138777 | 0.0134450 | 5.5357443 | 0.0100698 | -0.0123927 | 0.0127455 | 0.0014154 | 0.0013473 | 0.2934833 | -0.0014850 | 0.0133785 | 0.9116183 | 0.0831000 | 0.0844726 | 0.0154230 | 0.4970044 | Hearing | pre-pulse inhibition | -0.0163818 | 0.0001160 | -0.0166092 | -0.0161545 | -1.5209536 | 0.1282898 | 0.0154242 | 0.0000580 | 127 |
| % pre-pulse inhibition - ppi3 | Acoustic Startle and Pre-pulse Inhibition (PPI) | 8611 | 8635 | 5.6788790 | 0.0083258 | -0.0274295 | 0.0126776 | 5.6806335 | 0.0083262 | -0.0125107 | 0.0123495 | -0.0017545 | 0.0009756 | 0.0721416 | -0.0149188 | 0.0096554 | 0.1223374 | 0.0598191 | 0.0608737 | 0.0314267 | 0.5387274 | Hearing | pre-pulse inhibition | -0.0174772 | 0.0001160 | -0.0177046 | -0.0172499 | -1.6226531 | 0.1046819 | 0.0314371 | 0.0000580 | 128 |
| % pre-pulse inhibition - ppi4 | Acoustic Startle and Pre-pulse Inhibition (PPI) | 4200 | 4252 | 4.9935487 | 0.0197705 | -0.0558992 | 0.0483875 | 5.0065163 | 0.0197557 | -0.0573650 | 0.0472826 | -0.0129675 | 0.0026148 | 0.0000007 | 0.0014658 | 0.0271553 | 0.9569550 | 0.1157949 | 0.1074750 | 0.0628239 | 0.5470346 | Hearing | pre-pulse inhibition | 0.0745633 | 0.0002368 | 0.0740992 | 0.0750274 | 4.8453759 | 0.0000013 | 0.0629067 | 0.0001184 | 129 |
| 12khz-evoked abr threshold | Auditory Brain Stem Response | 3560 | 3763 | 3.4992286 | 0.1059298 | 0.0229517 | 0.0624052 | 3.4966054 | 0.1059421 | 0.0609302 | 0.0620518 | 0.0026232 | 0.0053570 | 0.6243831 | -0.0379784 | 0.0494810 | 0.4427980 | 0.1933031 | 0.2249660 | 0.0111476 | 0.9133130 | Hearing | abr threshold | -0.1516824 | 0.0002735 | -0.1522186 | -0.1511463 | -9.1710620 | 0.0000000 | 0.0111481 | 0.0001366 | 130 |
| 18khz-evoked abr threshold | Auditory Brain Stem Response | 3561 | 3758 | 3.4318421 | 0.0798253 | 0.0315473 | 0.0576694 | 3.4042233 | 0.0798455 | 0.0510466 | 0.0576433 | 0.0276188 | 0.0053528 | 0.0000003 | -0.0194992 | 0.0494211 | 0.6931897 | 0.1932762 | 0.2273478 | 0.0357667 | 0.8640465 | Hearing | abr threshold | -0.1623538 | 0.0002737 | -0.1628902 | -0.1618173 | -9.8138068 | 0.0000000 | 0.0357820 | 0.0001367 | 131 |
| 24khz-evoked abr threshold | Auditory Brain Stem Response | 3539 | 3751 | 3.5635330 | 0.0520808 | -0.0565129 | 0.0989027 | 3.5291020 | 0.0521159 | -0.0110715 | 0.0986830 | 0.0344311 | 0.0056684 | 0.0000000 | -0.0454414 | 0.0523940 | 0.3858175 | 0.2088828 | 0.2377726 | 0.0521665 | 0.7510437 | Hearing | abr threshold | -0.1295334 | 0.0002748 | -0.1300720 | -0.1289948 | -7.8138984 | 0.0000000 | 0.0522139 | 0.0001372 | 132 |
| 30khz-evoked abr threshold | Auditory Brain Stem Response | 3391 | 3648 | 3.9154021 | 0.0439635 | -0.0347744 | 0.0707535 | 3.8554164 | 0.0439755 | -0.0173863 | 0.0687008 | 0.0599857 | 0.0062369 | 0.0000000 | -0.0173881 | 0.0573692 | 0.7618328 | 0.2373050 | 0.2493483 | 0.0905502 | 0.6800193 | Hearing | abr threshold | -0.0494938 | 0.0002848 | -0.0500519 | -0.0489357 | -2.9330273 | 0.0033676 | 0.0907989 | 0.0001421 | 133 |
| 6khz-evoked abr threshold | Auditory Brain Stem Response | 3556 | 3764 | 3.8567602 | 0.0853804 | -0.0539256 | 0.0337384 | 3.8662195 | 0.0853863 | -0.0182438 | 0.0332689 | -0.0094593 | 0.0035933 | 0.0085021 | -0.0356818 | 0.0331587 | 0.2819365 | 0.1268049 | 0.1490493 | 0.0161577 | 0.9409867 | Hearing | abr threshold | -0.1616199 | 0.0002737 | -0.1621563 | -0.1610835 | -9.7697071 | 0.0000000 | 0.0161591 | 0.0001367 | 134 |
| click-evoked abr threshold | Auditory Brain Stem Response | 2158 | 2367 | 3.1189578 | 0.1345136 | -0.0864063 | 0.0543331 | 3.0948575 | 0.1345122 | -0.1737057 | 0.0547330 | 0.0241003 | 0.0050680 | 0.0000021 | 0.0872994 | 0.0566034 | 0.1231089 | 0.1458784 | 0.1568150 | 0.0367310 | 0.9468739 | Hearing | abr threshold | -0.0722724 | 0.0004435 | -0.0731417 | -0.0714031 | -3.4317330 | 0.0006051 | 0.0367475 | 0.0002211 | 135 |
| response amplitude - bn | Acoustic Startle and Pre-pulse Inhibition (PPI) | 8690 | 8697 | 2.2194713 | 0.4498338 | 0.6086231 | 0.1457406 | 2.3436881 | 0.4498226 | 0.6168208 | 0.1391363 | -0.1242168 | 0.0098935 | 0.0000000 | -0.0081978 | 0.0991009 | 0.9340740 | 0.6559561 | 0.5935907 | 0.0442726 | 0.9479141 | Hearing | response amplitude | 0.0999039 | 0.0001151 | 0.0996783 | 0.1001294 | 9.3133212 | 0.0000000 | 0.0443016 | 0.0000575 | 136 |
| response amplitude - pp1 | Acoustic Startle and Pre-pulse Inhibition (PPI) | 8690 | 8697 | 2.2217673 | 0.4448506 | 0.7467496 | 0.2060138 | 2.3468951 | 0.4448283 | 0.7094119 | 0.1987371 | -0.1251278 | 0.0114523 | 0.0000000 | 0.0373377 | 0.1143250 | 0.7439805 | 0.7646174 | 0.6582718 | 0.0485158 | 0.9327793 | Hearing | response amplitude | 0.1497577 | 0.0001151 | 0.1495322 | 0.1499832 | 13.9608369 | 0.0000000 | 0.0485540 | 0.0000575 | 137 |
| response amplitude - pp1_s | Acoustic Startle and Pre-pulse Inhibition (PPI) | 8616 | 8635 | 4.6640118 | 0.4860272 | 0.1606448 | 0.2091094 | 4.9653182 | 0.4860126 | 0.2379720 | 0.2067206 | -0.3013063 | 0.0093307 | 0.0000000 | -0.0773273 | 0.0916264 | 0.3987158 | 0.5668753 | 0.5549249 | 0.0717876 | 0.9642544 | Hearing | response amplitude | 0.0213068 | 0.0001160 | 0.0210794 | 0.0215341 | 1.9784889 | 0.0478895 | 0.0719113 | 0.0000580 | 138 |
| response amplitude - pp2 | Acoustic Startle and Pre-pulse Inhibition (PPI) | 8690 | 8697 | 2.5692044 | 0.4821516 | 0.5300609 | 0.0969582 | 2.6345133 | 0.4821541 | 0.4628846 | 0.0962204 | -0.0653090 | 0.0065857 | 0.0000000 | 0.0671763 | 0.0646322 | 0.2986529 | 0.3842801 | 0.4157404 | 0.0294292 | 0.9803652 | Hearing | response amplitude | -0.0786892 | 0.0001151 | -0.0789147 | -0.0784637 | -7.3356297 | 0.0000000 | 0.0294377 | 0.0000575 | 139 |
| response amplitude - pp2_s | Acoustic Startle and Pre-pulse Inhibition (PPI) | 8616 | 8635 | 4.2025132 | 0.4887333 | 0.2292927 | 0.2099351 | 4.4951692 | 0.4887185 | 0.2854435 | 0.2069940 | -0.2926558 | 0.0103505 | 0.0000000 | -0.0561512 | 0.1015788 | 0.5804195 | 0.6273260 | 0.6151479 | 0.0694201 | 0.9569443 | Hearing | response amplitude | 0.0196038 | 0.0001160 | 0.0193765 | 0.0198311 | 1.8203560 | 0.0687221 | 0.0695320 | 0.0000580 | 140 |
| response amplitude - pp3 | Acoustic Startle and Pre-pulse Inhibition (PPI) | 8690 | 8697 | 2.8110364 | 0.5068734 | 0.3600798 | 0.1059019 | 2.8884268 | 0.5068749 | 0.5022320 | 0.1046268 | -0.0773904 | 0.0071045 | 0.0000000 | -0.1421523 | 0.0695902 | 0.0410995 | 0.4171516 | 0.4387914 | 0.0273697 | 0.9798595 | Hearing | response amplitude | -0.0505743 | 0.0001151 | -0.0507998 | -0.0503487 | -4.7146779 | 0.0000024 | 0.0273765 | 0.0000575 | 141 |
| response amplitude - pp3_s | Acoustic Startle and Pre-pulse Inhibition (PPI) | 8616 | 8635 | 3.8852599 | 0.5089350 | 0.4336943 | 0.1928987 | 4.1475717 | 0.5089316 | 0.3516598 | 0.1903063 | -0.2623118 | 0.0103936 | 0.0000000 | 0.0820345 | 0.1020109 | 0.4213097 | 0.6244412 | 0.6275255 | 0.0617492 | 0.9592819 | Hearing | response amplitude | -0.0049270 | 0.0001160 | -0.0051543 | -0.0046997 | -0.4575061 | 0.6473131 | 0.0618278 | 0.0000580 | 142 |
| response amplitude - pp4 | Acoustic Startle and Pre-pulse Inhibition (PPI) | 4200 | 4253 | 2.5220619 | 0.8110791 | 0.0978923 | 0.1234483 | 2.5839777 | 0.8110827 | 0.1302859 | 0.1231000 | -0.0619157 | 0.0093739 | 0.0000000 | -0.0323936 | 0.0960420 | 0.7359109 | 0.3742047 | 0.4094718 | 0.0122408 | 0.9895842 | Hearing | response amplitude | -0.0900636 | 0.0002368 | -0.0905277 | -0.0895996 | -5.8529819 | 0.0000000 | 0.0122414 | 0.0001183 | 143 |
| response amplitude - pp4_s | Acoustic Startle and Pre-pulse Inhibition (PPI) | 4200 | 4253 | 3.1225363 | 0.7856679 | 0.1334547 | 0.3217119 | 3.3480981 | 0.7856700 | 0.1878389 | 0.3210710 | -0.2255618 | 0.0131753 | 0.0000000 | -0.0543842 | 0.1337303 | 0.6842617 | 0.5249596 | 0.5570354 | 0.0426057 | 0.9792942 | Hearing | response amplitude | -0.0593060 | 0.0002368 | -0.0597700 | -0.0588419 | -3.8541272 | 0.0001170 | 0.0426315 | 0.0001183 | 144 |
| response amplitude - s | Acoustic Startle and Pre-pulse Inhibition (PPI) | 8619 | 8637 | 4.9777667 | 0.4759428 | 0.1171862 | 0.2320353 | 5.2710722 | 0.4759356 | 0.1676378 | 0.2296406 | -0.2933055 | 0.0085056 | 0.0000000 | -0.0504517 | 0.0837298 | 0.5468152 | 0.5306732 | 0.4946283 | 0.0719112 | 0.9682856 | Hearing | response amplitude | 0.0703399 | 0.0001159 | 0.0701127 | 0.0705672 | 6.5325288 | 0.0000000 | 0.0720355 | 0.0000580 | 145 |
| aortic diameter (dao) | Echo | 1266 | 1225 | 0.2114822 | 0.0023111 | 0.2283281 | 0.0247147 | 0.2569989 | 0.0024016 | 0.2987865 | 0.0218207 | -0.0455167 | 0.0028363 | 0.0000000 | -0.0704584 | 0.0322580 | 0.0290587 | 0.0603588 | 0.0639535 | 0.4294652 | 0.5632409 | Heart | aortic diameter (dao) | -0.0578636 | 0.0008050 | -0.0594415 | -0.0562858 | -2.0393645 | 0.0415191 | 0.4592407 | 0.0004019 | 146 |
| cardiac output | Echo | 2963 | 1965 | 2.7219178 | 0.0733139 | 0.6652952 | 0.0561492 | 2.8130086 | 0.0734188 | 0.6870336 | 0.0598444 | -0.0910909 | 0.0075100 | 0.0000000 | -0.0217384 | 0.0795410 | 0.7846383 | 0.2036439 | 0.2122404 | 0.2596041 | 0.6591275 | Heart | cardiac output | -0.0414330 | 0.0004238 | -0.0422635 | -0.0406024 | -2.0127301 | 0.0441973 | 0.2656838 | 0.0002030 | 147 |
| cv | Electrocardiogram (ECG) | 4301 | 4295 | 0.6401225 | 0.1956047 | -0.0778906 | 0.2897378 | 0.3822708 | 0.1955811 | 0.0755486 | 0.2856489 | 0.2578516 | 0.0173377 | 0.0000000 | -0.1534392 | 0.1695412 | 0.3654807 | 0.7619599 | 0.7684299 | 0.1312976 | 0.6285531 | Heart | cv | -0.0084555 | 0.0002328 | -0.0089119 | -0.0079992 | -0.5541426 | 0.5794957 | 0.1320600 | 0.0001164 | 148 |
| ejection fraction | Echo | 3128 | 2139 | 4.1243352 | 0.1731866 | -0.0481894 | 0.0393739 | 4.1041463 | 0.1731904 | -0.0946772 | 0.0373523 | 0.0201889 | 0.0041411 | 0.0000011 | 0.0464878 | 0.0442819 | 0.2938635 | 0.1271845 | 0.1113276 | 0.0355001 | 0.9301714 | Heart | ejection fraction | 0.1330875 | 0.0003941 | 0.1323151 | 0.1338599 | 6.7041513 | 0.0000000 | 0.0355150 | 0.0001900 | 149 |
| end-diastolic diameter | Echo | 1558 | 563 | 1.3928268 | 0.0120731 | 0.2520762 | 0.0386471 | 1.4379623 | 0.0124057 | 0.3677807 | 0.0503957 | -0.0451356 | 0.0040847 | 0.0000000 | -0.1157045 | 0.0432932 | 0.0075995 | 0.0525149 | 0.0722362 | 0.4084308 | 0.6265383 | Heart | end-diastolic diameter | -0.3194144 | 0.0012144 | -0.3217945 | -0.3170342 | -9.1658652 | 0.0000000 | 0.4337264 | 0.0004721 | 150 |
| end-systolic diameter | Echo | 1558 | 563 | 1.0862341 | 0.0466011 | 0.2579154 | 0.0487940 | 1.1432994 | 0.0468842 | 0.4442890 | 0.0751665 | -0.0570653 | 0.0073946 | 0.0000000 | -0.1863736 | 0.0777262 | 0.0166010 | 0.0993965 | 0.1275504 | 0.2517515 | 0.6620820 | Heart | end-systolic diameter | -0.2499661 | 0.0012144 | -0.2523463 | -0.2475859 | -7.1729890 | 0.0000000 | 0.2572820 | 0.0004721 | 151 |
| fractional shortening | Echo | 3151 | 2163 | 3.5256497 | 0.2425705 | -0.0838483 | 0.0657367 | 3.4968378 | 0.2425772 | -0.1248801 | 0.0649814 | 0.0288120 | 0.0055940 | 0.0000003 | 0.0410318 | 0.0594661 | 0.4902295 | 0.1689068 | 0.1565902 | 0.0363860 | 0.9346703 | Heart | fractional shortening | 0.0756426 | 0.0003903 | 0.0748776 | 0.0764076 | 3.8287768 | 0.0001303 | 0.0364020 | 0.0001883 | 152 |
| heart weight | Heart Weight | 9886 | 9813 | 4.7607948 | 0.0295018 | 0.5542394 | 0.0611763 | 4.9399004 | 0.0295099 | 0.6329291 | 0.0612692 | -0.1791057 | 0.0017898 | 0.0000000 | -0.0786897 | 0.0157274 | 0.0000006 | 0.1118606 | 0.1244741 | 0.5387872 | 0.8254676 | Heart | heart weight | -0.1068449 | 0.0001016 | -0.1070440 | -0.1066458 | -10.6020969 | 0.0000000 | 0.6024451 | 0.0000508 | 153 |
| heart weight normalised against body weight | Heart Weight | 7967 | 7890 | 1.5992090 | 0.0315126 | -0.4376949 | 0.0662743 | 1.5608045 | 0.0315232 | -0.3822376 | 0.0663654 | 0.0384045 | 0.0020492 | 0.0000000 | -0.0554573 | 0.0171250 | 0.0012050 | 0.1154349 | 0.1292014 | 0.2771715 | 0.7629209 | Heart | heart weight | -0.1126666 | 0.0001262 | -0.1129139 | -0.1124193 | -10.0300578 | 0.0000000 | 0.2846156 | 0.0000631 | 154 |
| heart weight normalised against tibia length | Heart Weight | 139 | 129 | -0.2613254 | 0.0029684 | -0.8688421 | 0.0377659 | -0.4467379 | 0.0029454 | -0.9067467 | 0.0365703 | 0.1854125 | 0.0020579 | 0.0000000 | 0.0379046 | 0.0269525 | 0.1638057 | 0.0154228 | 0.0142236 | 0.9881755 | 0.9924102 | Heart | heart weight | 0.0806520 | 0.0076447 | 0.0656686 | 0.0956354 | 0.9224315 | 0.3571395 | 2.5624001 | 0.0037736 | 155 |
| hr | Echo | 9526 | 8437 | 6.4157589 | 0.0891563 | 0.0317825 | 0.0267460 | 6.4247723 | 0.0891571 | 0.0512092 | 0.0263269 | -0.0090133 | 0.0013020 | 0.0000000 | -0.0194265 | 0.0126255 | 0.1239056 | 0.0819866 | 0.0752169 | 0.0215634 | 0.9599306 | Heart | hr | 0.0861720 | 0.0001118 | 0.0859529 | 0.0863911 | 8.1501930 | 0.0000000 | 0.0215668 | 0.0000557 | 156 |
| hrv | Electrocardiogram (ECG) | 3950 | 3937 | 2.8067016 | 0.1904818 | -0.0162054 | 0.3359745 | 2.5562331 | 0.1905127 | 0.0368827 | 0.3295481 | 0.2504686 | 0.0176957 | 0.0000000 | -0.0530881 | 0.1754441 | 0.7622093 | 0.7414647 | 0.7566887 | 0.1357335 | 0.5841021 | Heart | hrv | -0.0203247 | 0.0002538 | -0.0208221 | -0.0198273 | -1.2758510 | 0.2020458 | 0.1365764 | 0.0001268 | 157 |
| lvawd | Echo | 1731 | 747 | -0.2345700 | 0.0909616 | 0.1862428 | 0.1282055 | -0.2035477 | 0.0909902 | 0.3329491 | 0.1301852 | -0.0310223 | 0.0064960 | 0.0000019 | -0.1467063 | 0.0656076 | 0.0254577 | 0.1175108 | 0.1127765 | 0.1382488 | 0.7760021 | Heart | lvawd | 0.0407403 | 0.0009614 | 0.0388560 | 0.0426246 | 1.3139337 | 0.1889902 | 0.1391398 | 0.0004040 | 158 |
| lvaws | Echo | 1708 | 723 | 0.1221399 | 0.1015899 | 0.2435782 | 0.0946284 | 0.1333103 | 0.1016406 | 0.2049267 | 0.1009408 | -0.0111704 | 0.0071241 | 0.1170545 | 0.0386516 | 0.0717351 | 0.5900814 | 0.1200648 | 0.1233268 | 0.0982939 | 0.7979743 | Heart | lvaws | -0.0272070 | 0.0009877 | -0.0291428 | -0.0252711 | -0.8657004 | 0.3867400 | 0.0986123 | 0.0004119 | 159 |
| lvidd | Echo | 3150 | 2163 | 1.2305035 | 0.1448166 | 0.2968726 | 0.0291501 | 1.2701259 | 0.1448261 | 0.2680476 | 0.0319868 | -0.0396225 | 0.0034830 | 0.0000000 | 0.0288250 | 0.0366051 | 0.4310569 | 0.0958561 | 0.1100951 | 0.1101921 | 0.9315413 | Heart | lvidd | -0.1385695 | 0.0003904 | -0.1393346 | -0.1378044 | -7.0134746 | 0.0000000 | 0.1106413 | 0.0001883 | 160 |
| lvids | Echo | 3150 | 2163 | 0.7750998 | 0.2927708 | 0.3564269 | 0.0856158 | 0.8432868 | 0.2927858 | 0.3709215 | 0.0883416 | -0.0681870 | 0.0066608 | 0.0000000 | -0.0144946 | 0.0698315 | 0.8355781 | 0.1855939 | 0.2049766 | 0.0815873 | 0.9373111 | Heart | lvids | -0.0994077 | 0.0003904 | -0.1001728 | -0.0986426 | -5.0313640 | 0.0000005 | 0.0817690 | 0.0001883 | 161 |
| lvpwd | Echo | 3150 | 2163 | -0.4233479 | 0.1114947 | 0.1875348 | 0.0982281 | -0.4035342 | 0.1114942 | 0.2510717 | 0.0981062 | -0.0198137 | 0.0030173 | 0.0000000 | -0.0635369 | 0.0314662 | 0.0435266 | 0.0871479 | 0.0807443 | 0.0936862 | 0.9268952 | Heart | lvpwd | 0.0762467 | 0.0003904 | 0.0754816 | 0.0770118 | 3.8591069 | 0.0001152 | 0.0939618 | 0.0001883 | 162 |
| lvpws | Echo | 3127 | 2139 | -0.2211764 | 0.2020145 | 0.2034083 | 0.0776996 | -0.2048843 | 0.2020132 | 0.2052047 | 0.0767605 | -0.0162922 | 0.0033319 | 0.0000010 | -0.0017965 | 0.0352788 | 0.9593902 | 0.1009518 | 0.0849820 | 0.0502974 | 0.9670257 | Heart | lvpws | 0.1721298 | 0.0003941 | 0.1713573 | 0.1729023 | 8.6703024 | 0.0000000 | 0.0503399 | 0.0001900 | 163 |
| mean r amplitude | Electrocardiogram (ECG) | 4456 | 4380 | -0.5062665 | 0.1581995 | 0.2038606 | 0.1559063 | -0.6284037 | 0.1582734 | 0.6187483 | 0.1509311 | 0.1221373 | 0.0129747 | 0.0000000 | -0.4148877 | 0.1278676 | 0.0011809 | 0.5464913 | 0.5777762 | 0.0979512 | 0.7096856 | Heart | mean r amplitude | -0.0556701 | 0.0002265 | -0.0561140 | -0.0552261 | -3.6988862 | 0.0002179 | 0.0982663 | 0.0001132 | 164 |
| mean sr amplitude | Electrocardiogram (ECG) | 3946 | 3935 | -0.2337628 | 0.1220771 | 0.2012503 | 0.2404819 | -0.3590224 | 0.1221262 | 0.4246722 | 0.2359611 | 0.1252596 | 0.0134405 | 0.0000000 | -0.2234219 | 0.1320107 | 0.0906051 | 0.5257887 | 0.5661949 | 0.0940232 | 0.6910604 | Heart | mean sr amplitude | -0.0740393 | 0.0002540 | -0.0745370 | -0.0735415 | -4.6459230 | 0.0000034 | 0.0943018 | 0.0001269 | 165 |
| pnn5(6>ms) | Electrocardiogram (ECG) | 2978 | 2907 | -1.1533004 | 0.5919332 | -0.0087992 | 1.5434375 | -1.9988875 | 0.5918158 | 0.0689280 | 1.4867335 | 0.8455872 | 0.0779029 | 0.0000000 | -0.0777272 | 0.8033360 | 0.9229244 | 3.0619818 | 2.7400596 | 0.1253275 | 0.5063225 | Heart | pnn5(6>ms) | 0.1110786 | 0.0003402 | 0.1104117 | 0.1117454 | 6.0219252 | 0.0000000 | 0.1259899 | 0.0001700 | 166 |
| pq | Electrocardiogram (ECG) | 3950 | 3937 | 3.0150161 | 0.0259727 | -0.1462911 | 0.0685100 | 3.0161647 | 0.0259623 | -0.0262765 | 0.0667223 | -0.0011486 | 0.0033223 | 0.7295670 | -0.1200145 | 0.0331242 | 0.0002931 | 0.1476002 | 0.1364866 | 0.0567105 | 0.4160728 | Heart | pq | 0.0782803 | 0.0002538 | 0.0777829 | 0.0787777 | 4.9139134 | 0.0000009 | 0.0567714 | 0.0001268 | 167 |
| pr | Electrocardiogram (ECG) | 6377 | 6275 | 3.4522997 | 0.0753581 | 0.0035440 | 0.0353978 | 3.4452992 | 0.0753574 | 0.0316908 | 0.0349761 | 0.0070005 | 0.0019925 | 0.0004441 | -0.0281467 | 0.0189408 | 0.1372993 | 0.1090364 | 0.1045885 | 0.0198411 | 0.8694502 | Heart | pr | 0.0416475 | 0.0001582 | 0.0413375 | 0.0419575 | 3.3115895 | 0.0009303 | 0.0198437 | 0.0000791 | 168 |
| qrs | Electrocardiogram (ECG) | 6327 | 6274 | 2.3480554 | 0.0481079 | 0.0196999 | 0.0421127 | 2.3387376 | 0.0481092 | 0.0231856 | 0.0420160 | 0.0093178 | 0.0016258 | 0.0000000 | -0.0034857 | 0.0153526 | 0.8203952 | 0.0835155 | 0.0875771 | 0.0336588 | 0.8322082 | Heart | qrs | -0.0474889 | 0.0001588 | -0.0478001 | -0.0471777 | -3.7685328 | 0.0001650 | 0.0336715 | 0.0000794 | 169 |
| qtc | Electrocardiogram (ECG) | 5179 | 5078 | 4.1158801 | 0.1547600 | -0.0193474 | 0.0386589 | 4.1144485 | 0.1547592 | 0.0221974 | 0.0385312 | 0.0014316 | 0.0014828 | 0.3343172 | -0.0415448 | 0.0144201 | 0.0039733 | 0.0668350 | 0.0672820 | 0.0055734 | 0.9855648 | Heart | qtc | -0.0066674 | 0.0001951 | -0.0070498 | -0.0062850 | -0.4773140 | 0.6331487 | 0.0055734 | 0.0000975 | 170 |
| qtc dispersion | Electrocardiogram (ECG) | 4457 | 4382 | 2.6481835 | 0.4773977 | -0.0075252 | 0.1020346 | 2.6488990 | 0.4774054 | -0.1957964 | 0.0915757 | -0.0007155 | 0.0101636 | 0.9438776 | 0.1882712 | 0.1018002 | 0.0644356 | 0.4864224 | 0.4537158 | 0.0133347 | 0.9033263 | Heart | qtc dispersion | 0.0696043 | 0.0002264 | 0.0691605 | 0.0700481 | 4.6255103 | 0.0000038 | 0.0133355 | 0.0001132 | 171 |
| respiration rate | Echo | 2282 | 1568 | 5.0372826 | 0.3093590 | 0.2006452 | 0.0929456 | 5.0822189 | 0.3093941 | 0.3489934 | 0.0676138 | -0.0449363 | 0.0104854 | 0.0000188 | -0.1483482 | 0.1124575 | 0.1872155 | 0.3080381 | 0.2110916 | 0.0582075 | 0.8593281 | Heart | respiration rate | 0.3778312 | 0.0005389 | 0.3767750 | 0.3788874 | 16.2761042 | 0.0000000 | 0.0582734 | 0.0002599 | 172 |
| rmssd | Electrocardiogram (ECG) | 3950 | 3937 | 0.3946741 | 0.1687611 | -0.2818053 | 0.5694372 | 0.2425925 | 0.1688217 | -0.0809915 | 0.5644746 | 0.1520816 | 0.0169613 | 0.0000000 | -0.2008138 | 0.1683480 | 0.2329696 | 0.7107537 | 0.7253639 | 0.0904519 | 0.5615006 | Heart | rmssd | -0.0203479 | 0.0002538 | -0.0208453 | -0.0198505 | -1.2773054 | 0.2015321 | 0.0906998 | 0.0001268 | 173 |
| rr | Electrocardiogram (ECG) | 6377 | 6275 | 4.4821847 | 0.0921813 | -0.0210802 | 0.0267909 | 4.4721304 | 0.0921814 | -0.0051946 | 0.0266164 | 0.0100543 | 0.0009588 | 0.0000000 | -0.0158856 | 0.0091075 | 0.0811482 | 0.0517933 | 0.0506040 | 0.0220576 | 0.9765734 | Heart | rr | 0.0232277 | 0.0001582 | 0.0229177 | 0.0235377 | 1.8469406 | 0.0647791 | 0.0220612 | 0.0000791 | 174 |
| st | Electrocardiogram (ECG) | 5499 | 5491 | 3.1782096 | 0.2622603 | -0.0159427 | 0.0560079 | 3.1751253 | 0.2622644 | 0.0503377 | 0.0556851 | 0.0030843 | 0.0020876 | 0.1395903 | -0.0662805 | 0.0192783 | 0.0005884 | 0.1008231 | 0.0988082 | 0.0075115 | 0.9858274 | Heart | st | 0.0201867 | 0.0001821 | 0.0198298 | 0.0205435 | 1.4959925 | 0.1346843 | 0.0075117 | 0.0000910 | 175 |
| stroke volume | Echo | 2964 | 1965 | 3.4019972 | 0.1777823 | 0.6563882 | 0.0597945 | 3.4858923 | 0.1778278 | 0.5270173 | 0.0650054 | -0.0838951 | 0.0073662 | 0.0000000 | 0.1293709 | 0.0784357 | 0.0991458 | 0.1979902 | 0.2145533 | 0.1676915 | 0.8469544 | Heart | stroke volume | -0.0804264 | 0.0004237 | -0.0812569 | -0.0795960 | -3.9072164 | 0.0000946 | 0.1692904 | 0.0002030 | 176 |
| basophil cell count | Hematology | 4440 | 4413 | -3.3690397 | 0.1629587 | 0.0184881 | 0.0856009 | -3.2251669 | 0.1629652 | 0.3278062 | 0.0837009 | -0.1438727 | 0.0091235 | 0.0000000 | -0.3093181 | 0.0843773 | 0.0002481 | 0.3933789 | 0.3946229 | 0.0938273 | 0.8767913 | Hematology | basophil count | -0.0031579 | 0.0002261 | -0.0036010 | -0.0027149 | -0.2100317 | 0.8336478 | 0.0941041 | 0.0001130 | 177 |
| basophil differential count | Hematology | 4577 | 4518 | -1.1285379 | 0.2646096 | 0.1402307 | 0.1947576 | -1.1049786 | 0.2646038 | 0.1553653 | 0.1901673 | -0.0235594 | 0.0164320 | 0.1516831 | -0.0151345 | 0.1539611 | 0.9216959 | 0.7540315 | 0.7022376 | 0.0143310 | 0.8419205 | Hematology | basophil count | 0.0711609 | 0.0002201 | 0.0707296 | 0.0715922 | 4.7970636 | 0.0000016 | 0.0143320 | 0.0001100 | 178 |
| eosinophil cell count | Hematology | 4465 | 4431 | -2.0683217 | 0.2046669 | -0.0910317 | 0.1069856 | -1.8585401 | 0.2046755 | -0.0631752 | 0.1054107 | -0.2097816 | 0.0102498 | 0.0000000 | -0.0278565 | 0.0947612 | 0.7687923 | 0.4431435 | 0.4448120 | 0.1052956 | 0.8957603 | Hematology | eosinophils | -0.0037590 | 0.0002250 | -0.0042000 | -0.0033181 | -0.2506163 | 0.8021166 | 0.1056873 | 0.0001124 | 179 |
| eosinophil differential count | Hematology | 4618 | 4555 | 0.5520474 | 0.2663528 | -0.1738467 | 0.1639997 | 0.5609987 | 0.2663509 | -0.0982735 | 0.1601987 | -0.0089513 | 0.0137075 | 0.5137602 | -0.0755733 | 0.1279495 | 0.5547725 | 0.6192599 | 0.5815746 | 0.0116712 | 0.8869295 | Hematology | eosinophils | 0.0627842 | 0.0002182 | 0.0623566 | 0.0632119 | 4.2504886 | 0.0000215 | 0.0116717 | 0.0001091 | 180 |
| eosinophils | Immunophenotyping | 1050 | 1048 | 7.1615018 | 0.2160291 | -0.2685389 | 0.1596616 | 7.1040208 | 0.2161640 | 0.1213628 | 0.1617609 | 0.0574811 | 0.0197350 | 0.0036308 | -0.3899017 | 0.1940747 | 0.0446928 | 0.3710890 | 0.4504276 | 0.0396105 | 0.8857808 | Hematology | eosinophils | -0.1937565 | 0.0009560 | -0.1956303 | -0.1918828 | -6.2664560 | 0.0000000 | 0.0396313 | 0.0004773 | 181 |
| hematocrit | Hematology | 9685 | 9560 | 3.8995964 | 0.0152302 | -0.0255214 | 0.0115822 | 3.9123589 | 0.0152337 | -0.0015196 | 0.0116661 | -0.0127625 | 0.0008680 | 0.0000000 | -0.0240018 | 0.0076942 | 0.0018148 | 0.0525649 | 0.0577891 | 0.0520960 | 0.9038619 | Hematology | hematocrit | -0.0947540 | 0.0001040 | -0.0949578 | -0.0945503 | -9.2931905 | 0.0000000 | 0.0521432 | 0.0000520 | 182 |
| hemoglobin | Hematology | 9686 | 9560 | 2.6824573 | 0.0121341 | -0.0202465 | 0.0111419 | 2.6838930 | 0.0121431 | 0.0079598 | 0.0116843 | -0.0014357 | 0.0008632 | 0.0962977 | -0.0282062 | 0.0075864 | 0.0002014 | 0.0458148 | 0.0628808 | 0.0177487 | 0.8791673 | Hematology | hemoglobin | -0.3166353 | 0.0001040 | -0.3168390 | -0.3164315 | -31.0554296 | 0.0000000 | 0.0177506 | 0.0000520 | 183 |
| large unstained cell (luc) count | Hematology | 3288 | 3286 | -3.0191457 | 0.0801670 | 0.1453914 | 0.1232269 | -2.6791935 | 0.0801999 | 0.4030197 | 0.1205977 | -0.3399522 | 0.0111381 | 0.0000000 | -0.2576283 | 0.1082887 | 0.0173869 | 0.4119185 | 0.4136492 | 0.2796309 | 0.7445418 | Hematology | luc | -0.0041928 | 0.0003045 | -0.0047896 | -0.0035960 | -0.2402738 | 0.8101255 | 0.2872816 | 0.0001522 | 184 |
| large unstained cell (luc) differential count | Hematology | 3290 | 3285 | -0.2957064 | 0.0708369 | 0.2411835 | 0.0751018 | -0.1841867 | 0.0708514 | 0.2896532 | 0.0723252 | -0.1115197 | 0.0084221 | 0.0000000 | -0.0484697 | 0.0813354 | 0.5512485 | 0.3072204 | 0.3175074 | 0.1254753 | 0.7744827 | Hematology | luc | -0.0329358 | 0.0003045 | -0.0335326 | -0.0323391 | -1.8875710 | 0.0591276 | 0.1261401 | 0.0001522 | 185 |
| lymphocyte cell count | Hematology | 4465 | 4431 | 1.6226022 | 0.0487100 | 0.0194338 | 0.0606233 | 1.8403041 | 0.0486939 | -0.0780460 | 0.0569039 | -0.2177019 | 0.0062415 | 0.0000000 | 0.0974798 | 0.0575615 | 0.0904029 | 0.2781778 | 0.2586089 | 0.2663512 | 0.7551449 | Hematology | lymphocytes | 0.0729428 | 0.0002250 | 0.0725018 | 0.0733837 | 4.8631205 | 0.0000012 | 0.2729323 | 0.0001124 | 186 |
| lymphocyte differential count | Hematology | 4719 | 4654 | 4.4241867 | 0.0094655 | 0.0021079 | 0.0098409 | 4.4042274 | 0.0095404 | -0.0648179 | 0.0147155 | 0.0199593 | 0.0017131 | 0.0000000 | 0.0669258 | 0.0155010 | 0.0000160 | 0.0485153 | 0.0972239 | 0.1089159 | 0.6747591 | Hematology | lymphocytes | -0.6951387 | 0.0002135 | -0.6955572 | -0.6947202 | -47.5714383 | 0.0000000 | 0.1093496 | 0.0001067 | 187 |
| mean cell hemoglobin concentration | Hematology | 9674 | 9555 | 3.3886402 | 0.0104332 | 0.0029860 | 0.0032125 | 3.3775069 | 0.0104364 | 0.0055138 | 0.0038801 | 0.0111333 | 0.0004869 | 0.0000000 | -0.0025278 | 0.0042451 | 0.5515406 | 0.0248858 | 0.0363859 | 0.0581770 | 0.9465490 | Hematology | mean cell hemoglobin concentration | -0.3798852 | 0.0001040 | -0.3800891 | -0.3796813 | -37.2425683 | 0.0000000 | 0.0582427 | 0.0000520 | 188 |
| mean cell volume | Hematology | 9703 | 9574 | 3.8872055 | 0.0096606 | 0.0117795 | 0.0043139 | 3.8808377 | 0.0096612 | 0.0122404 | 0.0043060 | 0.0063678 | 0.0002955 | 0.0000000 | -0.0004609 | 0.0026258 | 0.8606749 | 0.0184184 | 0.0190366 | 0.0535267 | 0.9573603 | Hematology | mean cell volume | -0.0330143 | 0.0001038 | -0.0332178 | -0.0328109 | -3.2406337 | 0.0011947 | 0.0535779 | 0.0000519 | 189 |
| mean corpuscular hemoglobin | Hematology | 9654 | 9537 | 2.6766329 | 0.0083496 | 0.0144073 | 0.0042481 | 2.6589882 | 0.0083540 | 0.0195738 | 0.0047742 | 0.0176447 | 0.0005035 | 0.0000000 | -0.0051664 | 0.0044021 | 0.2405539 | 0.0256827 | 0.0373869 | 0.1046301 | 0.9299778 | Hematology | mean corpuscular hemoglobin | -0.3755031 | 0.0001043 | -0.3757075 | -0.3752988 | -36.7765846 | 0.0000000 | 0.1050145 | 0.0000521 | 190 |
| mean platelet volume | Hematology | 7512 | 7457 | 1.8601220 | 0.0513522 | 0.0172769 | 0.0112344 | 1.8485135 | 0.0513523 | 0.0283444 | 0.0114351 | 0.0116085 | 0.0008119 | 0.0000000 | -0.0110675 | 0.0084395 | 0.1897484 | 0.0451275 | 0.0453736 | 0.0255006 | 0.9827353 | Hematology | mean platelet volume | -0.0054402 | 0.0001337 | -0.0057022 | -0.0051782 | -0.4705489 | 0.6379698 | 0.0255061 | 0.0000668 | 191 |
| monocyte cell count | Hematology | 4467 | 4431 | -2.1028067 | 0.0840817 | 0.4750689 | 0.1072548 | -1.8576850 | 0.0840965 | 0.3625824 | 0.1058859 | -0.2451217 | 0.0095119 | 0.0000000 | 0.1124865 | 0.0878056 | 0.2002021 | 0.4082275 | 0.4052992 | 0.2107264 | 0.7542768 | Hematology | monocytes | 0.0071981 | 0.0002249 | 0.0067573 | 0.0076390 | 0.4799559 | 0.6312705 | 0.2139314 | 0.0001124 | 192 |
| monocyte differential count | Hematology | 4720 | 4654 | 0.7222596 | 0.0687632 | 0.4509245 | 0.0918311 | 0.7308505 | 0.0687710 | 0.3858226 | 0.0907725 | -0.0085909 | 0.0068178 | 0.2076813 | 0.0651019 | 0.0636230 | 0.3062235 | 0.3019184 | 0.2931205 | 0.0939622 | 0.7745554 | Hematology | monocytes | 0.0295716 | 0.0002135 | 0.0291532 | 0.0299901 | 2.0238257 | 0.0430164 | 0.0942402 | 0.0001067 | 193 |
| monocytes | Immunophenotyping | 1009 | 1012 | 7.9886817 | 0.1354800 | -0.2741152 | 0.1743966 | 8.1033879 | 0.1356745 | 0.8150275 | 0.1746358 | -0.1147062 | 0.0191440 | 0.0000000 | -1.0891427 | 0.1882313 | 0.0000000 | 0.3594855 | 0.4280091 | 0.1127276 | 0.8487775 | Hematology | monocytes | -0.1744692 | 0.0009926 | -0.1764146 | -0.1725238 | -5.5378454 | 0.0000000 | 0.1132088 | 0.0004955 | 194 |
| neutrophil cell count | Hematology | 4466 | 4428 | -0.4763834 | 0.0880243 | -0.0437486 | 0.0845098 | -0.2003416 | 0.0881091 | 0.2020026 | 0.0901326 | -0.2760419 | 0.0084673 | 0.0000000 | -0.2457512 | 0.0773553 | 0.0014944 | 0.3162831 | 0.4016898 | 0.2355178 | 0.7915822 | Hematology | neutrophils | -0.2390434 | 0.0002250 | -0.2394845 | -0.2386024 | -15.9352865 | 0.0000000 | 0.2400233 | 0.0001125 | 195 |
| neutrophil differential count | Hematology | 4655 | 4610 | 2.2942157 | 0.0626576 | -0.0315861 | 0.0649079 | 2.3480996 | 0.0627398 | 0.2742317 | 0.0704597 | -0.0538840 | 0.0065138 | 0.0000000 | -0.3058177 | 0.0597124 | 0.0000003 | 0.2358550 | 0.3195010 | 0.0713321 | 0.7970769 | Hematology | neutrophils | -0.3035442 | 0.0002160 | -0.3039675 | -0.3031208 | -20.6530311 | 0.0000000 | 0.0714535 | 0.0001080 | 196 |
| neutrophils | Immunophenotyping | 1050 | 1048 | 8.6577526 | 0.1983490 | -0.2050511 | 0.2319183 | 8.6699764 | 0.1985364 | -0.1041723 | 0.2366943 | -0.0122238 | 0.0184164 | 0.5069423 | -0.1008787 | 0.1802609 | 0.5758086 | 0.3248758 | 0.4380117 | 0.0208368 | 0.8741465 | Hematology | neutrophils | -0.2988036 | 0.0009560 | -0.3006774 | -0.2969298 | -9.6638792 | 0.0000000 | 0.0208398 | 0.0004773 | 197 |
| platelet count | Hematology | 9637 | 9528 | 6.8938012 | 0.0367763 | 0.0496845 | 0.0264337 | 7.0766028 | 0.0367882 | -0.0664679 | 0.0265198 | -0.1828016 | 0.0026642 | 0.0000000 | 0.1161524 | 0.0236380 | 0.0000009 | 0.1640864 | 0.1775867 | 0.2809740 | 0.8517474 | Hematology | platelet count | -0.0790663 | 0.0001044 | -0.0792709 | -0.0788617 | -7.7384789 | 0.0000000 | 0.2887392 | 0.0000522 | 198 |
| red blood cell count | Hematology | 9689 | 9572 | 2.3145567 | 0.0108135 | -0.0401396 | 0.0124886 | 2.3336774 | 0.0108208 | -0.0165753 | 0.0127400 | -0.0191208 | 0.0008742 | 0.0000000 | -0.0235642 | 0.0077302 | 0.0023047 | 0.0505294 | 0.0603100 | 0.0906164 | 0.8705254 | Hematology | red blood cell count | -0.1769437 | 0.0001039 | -0.1771473 | -0.1767401 | -17.3613650 | 0.0000000 | 0.0908657 | 0.0000519 | 199 |
| red blood cell distribution width | Hematology | 7553 | 7496 | 2.6299218 | 0.0391453 | -0.0217161 | 0.0111071 | 2.6467269 | 0.0391466 | 0.0202995 | 0.0117527 | -0.0168050 | 0.0006859 | 0.0000000 | -0.0420156 | 0.0071281 | 0.0000000 | 0.0331076 | 0.0439563 | 0.0464590 | 0.9783984 | Hematology | red blood cell distribution width | -0.2834344 | 0.0001330 | -0.2836950 | -0.2831738 | -24.5811181 | 0.0000000 | 0.0464925 | 0.0000665 | 200 |
| white blood cell count | Hematology | 9368 | 9229 | 1.7932097 | 0.0451365 | -0.0497918 | 0.0516348 | 2.0360225 | 0.0451444 | -0.0053606 | 0.0504951 | -0.2428127 | 0.0039780 | 0.0000000 | -0.0444311 | 0.0354946 | 0.2106706 | 0.2610349 | 0.2468127 | 0.2997341 | 0.7793959 | Hematology | white blood cell count | 0.0560233 | 0.0001076 | 0.0558125 | 0.0562342 | 5.4012375 | 0.0000001 | 0.3092274 | 0.0000538 | 201 |
| b cell total | FACS | 293 | 288 | 11.9075262 | 0.0557141 | 0.1192383 | 0.2343652 | 11.8386643 | 0.0612105 | 0.5613281 | 0.3926168 | 0.0688619 | 0.0410855 | 0.0943664 | -0.4420897 | 0.4479749 | 0.3241990 | 0.2884615 | 0.5261263 | 0.0792306 | 0.7545951 | Immunology | B cells | -0.6010099 | 0.0034785 | -0.6078277 | -0.5941921 | -10.1902338 | 0.0000000 | 0.0793970 | 0.0017301 | 202 |
| b cells | Immunophenotyping | 751 | 754 | 12.1695842 | 0.1888646 | 0.1299576 | 0.1440820 | 12.1961181 | 0.1889108 | -0.0037736 | 0.1393928 | -0.0265339 | 0.0135369 | 0.0502150 | 0.1337312 | 0.1331417 | 0.3153769 | 0.2279992 | 0.2645174 | 0.0254048 | 0.9154600 | Immunology | B cells | -0.1485623 | 0.0013342 | -0.1511773 | -0.1459473 | -4.0671818 | 0.0000501 | 0.0254103 | 0.0006658 | 203 |
| b1 total | FACS | 293 | 288 | 9.0443001 | 0.0757515 | 0.3007998 | 0.2620987 | 8.7975455 | 0.0773813 | 0.2346821 | 0.3308912 | 0.2467546 | 0.0378233 | 0.0000000 | 0.0661176 | 0.4103579 | 0.8720641 | 0.3287590 | 0.4256165 | 0.1593293 | 0.8777272 | Immunology | B cells | -0.2582441 | 0.0034785 | -0.2650619 | -0.2514263 | -4.3785759 | 0.0000142 | 0.1606985 | 0.0017301 | 204 |
| b1b cells | Immunophenotyping | 742 | 745 | 8.6877658 | 0.2785105 | 0.0638644 | 0.1607232 | 8.5961612 | 0.2786710 | 0.0735437 | 0.1665747 | 0.0916046 | 0.0183596 | 0.0000007 | -0.0096793 | 0.1790960 | 0.9569082 | 0.2713797 | 0.3810144 | 0.0441539 | 0.9503101 | Immunology | B cells | -0.3393155 | 0.0013504 | -0.3419623 | -0.3366687 | -9.2334779 | 0.0000000 | 0.0441826 | 0.0006739 | 205 |
| b2 immature + mzb | FACS | 268 | 283 | 9.1961877 | 0.0729537 | 0.3554106 | 0.2305520 | 9.1365119 | 0.0766619 | 0.3973557 | 0.3797597 | 0.0596758 | 0.0402555 | 0.1389073 | -0.0419451 | 0.4344392 | 0.9231255 | 0.2729488 | 0.5045630 | 0.0578832 | 0.8436473 | Immunology | B cells | -0.6143074 | 0.0036725 | -0.6215054 | -0.6071094 | -10.1368831 | 0.0000000 | 0.0579479 | 0.0018248 | 206 |
| b2 mature | FACS | 268 | 282 | 11.7150885 | 0.0607348 | 0.1737397 | 0.2957477 | 11.6572359 | 0.0664893 | 0.6337603 | 0.4497526 | 0.0578526 | 0.0481710 | 0.2303727 | -0.4600206 | 0.5264141 | 0.3826399 | 0.3576649 | 0.5970362 | 0.0726091 | 0.7137416 | Immunology | B cells | -0.5122866 | 0.0036789 | -0.5194971 | -0.5050760 | -8.4460482 | 0.0000000 | 0.0727372 | 0.0018282 | 207 |
| b2 total | FACS | 293 | 288 | 11.8312272 | 0.0576383 | 0.1376320 | 0.2529784 | 11.7788146 | 0.0638625 | 0.6114319 | 0.4242054 | 0.0524126 | 0.0443397 | 0.2377539 | -0.4737998 | 0.4840280 | 0.3281299 | 0.3118801 | 0.5693769 | 0.0710051 | 0.7375941 | Immunology | B cells | -0.6019540 | 0.0034785 | -0.6087718 | -0.5951363 | -10.2062414 | 0.0000000 | 0.0711248 | 0.0017301 | 208 |
| b2b cells | Immunophenotyping | 742 | 745 | 12.1320532 | 0.1853182 | 0.0994120 | 0.1349689 | 12.1595495 | 0.1854559 | -0.0280921 | 0.1435363 | -0.0274963 | 0.0123456 | 0.0261220 | 0.1275041 | 0.1190116 | 0.2842287 | 0.1664918 | 0.2683942 | 0.0245448 | 0.9314256 | Immunology | B cells | -0.4775083 | 0.0013504 | -0.4801552 | -0.4748615 | -12.9939925 | 0.0000000 | 0.0245497 | 0.0006739 | 209 |
| cd24+ cd4 t cells | Immunophenotyping | 74 | 77 | 7.1494001 | 0.1950593 | 0.4454847 | 1.0560506 | 7.0009988 | 0.1953868 | -1.0093871 | 0.9911212 | 0.1484013 | 0.0492958 | 0.0031810 | 1.4548718 | 0.7607245 | 0.0581979 | 0.2826177 | 0.3101040 | 0.0948942 | 0.9554532 | Immunology | cd4 t | -0.0925271 | 0.0137990 | -0.1195727 | -0.0654816 | -0.7876716 | 0.4321401 | 0.0951806 | 0.0067568 | 210 |
| cd24+ cd8 t cells | Immunophenotyping | 74 | 77 | 7.3203001 | 0.1610511 | 0.5002081 | 1.1024872 | 7.2093638 | 0.1614008 | -0.5902486 | 1.0341980 | 0.1109363 | 0.0512632 | 0.0324414 | 1.0904567 | 0.7901568 | 0.1701367 | 0.2959759 | 0.3209977 | 0.0835543 | 0.9252060 | Immunology | cd4 t | -0.0808704 | 0.0137990 | -0.1079160 | -0.0538248 | -0.6884393 | 0.4922466 | 0.0837495 | 0.0067568 | 211 |
| cd4 cd25- nkt cells | Immunophenotyping | 546 | 539 | 7.2411180 | 0.3182798 | 0.3781815 | 0.2410114 | 6.9833950 | 0.3183151 | -0.0116871 | 0.2175496 | 0.2577231 | 0.0240340 | 0.0000000 | 0.3898686 | 0.3041703 | 0.2002746 | 0.3561094 | 0.3751721 | 0.1375573 | 0.9231846 | Immunology | cd4 nkt | -0.0521588 | 0.0018536 | -0.0557918 | -0.0485257 | -1.2114734 | 0.2259783 | 0.1384349 | 0.0009242 | 212 |
| cd4 cd25- t cells | Immunophenotyping | 686 | 685 | 10.5194246 | 0.1560673 | -0.2438493 | 0.1509031 | 10.5116901 | 0.1562451 | -0.0722039 | 0.1505003 | 0.0077345 | 0.0172409 | 0.6537999 | -0.1716454 | 0.1747990 | 0.3263405 | 0.2643194 | 0.3329148 | 0.0360930 | 0.8589550 | Immunology | cd4 t | -0.2307294 | 0.0014652 | -0.2336011 | -0.2278576 | -6.0277348 | 0.0000000 | 0.0361086 | 0.0007310 | 213 |
| cd4 cd25+ nkt cells | Immunophenotyping | 612 | 608 | 4.0609694 | 0.3797497 | 0.4198500 | 0.2895606 | 3.9775026 | 0.3797483 | 0.0643466 | 0.2669758 | 0.0834668 | 0.0257881 | 0.0012511 | 0.3555034 | 0.2921325 | 0.2239333 | 0.4226782 | 0.4110806 | 0.0378586 | 0.9473945 | Immunology | cd4 nkt | 0.0278165 | 0.0016475 | 0.0245875 | 0.0310455 | 0.6853214 | 0.4932715 | 0.0378767 | 0.0008217 | 214 |
| cd4 cd25+ t cells | Immunophenotyping | 686 | 685 | 8.1246559 | 0.3311549 | 0.0639640 | 0.1731040 | 8.1741678 | 0.3313160 | -0.2816967 | 0.1811130 | -0.0495119 | 0.0208391 | 0.0176797 | 0.3456607 | 0.2102409 | 0.1004435 | 0.2936186 | 0.4154848 | 0.0280064 | 0.9454050 | Immunology | cd4 t | -0.3471655 | 0.0014652 | -0.3500372 | -0.3442938 | -9.0695924 | 0.0000000 | 0.0280138 | 0.0007310 | 215 |
| cd4 cd44-cd62l- t cells | Immunophenotyping | 447 | 444 | 7.6706659 | 0.5750501 | -0.0118450 | 0.2634042 | 7.6915821 | 0.5752842 | 0.2644828 | 0.2763328 | -0.0209162 | 0.0322852 | 0.5172932 | -0.2763278 | 0.3486498 | 0.4283041 | 0.3909157 | 0.5241248 | 0.0178362 | 0.9464884 | Immunology | cd4 t | -0.2932456 | 0.0022599 | -0.2976749 | -0.2888162 | -6.1685857 | 0.0000000 | 0.0178381 | 0.0011261 | 216 |
| cd4 cd44-cd62l+ nkt cells | Immunophenotyping | 686 | 685 | 2.6344443 | 0.5536409 | 0.2159021 | 0.4312816 | 2.5410165 | 0.5537445 | -0.8248785 | 0.4193210 | 0.0934278 | 0.0288402 | 0.0012340 | 1.0407806 | 0.2949890 | 0.0004360 | 0.4604860 | 0.5285368 | 0.0478228 | 0.9507515 | Immunology | cd4 nkt | -0.1378311 | 0.0014652 | -0.1407028 | -0.1349593 | -3.6007944 | 0.0003286 | 0.0478593 | 0.0007310 | 217 |
| cd4 cd44-cd62l+ t cells | Immunophenotyping | 612 | 608 | 9.4051911 | 0.2402366 | -0.0841436 | 0.2123958 | 9.4237390 | 0.2403186 | -0.1923225 | 0.2165683 | -0.0185479 | 0.0213902 | 0.3860936 | 0.1081788 | 0.2384193 | 0.6501237 | 0.3097981 | 0.3818699 | 0.0244726 | 0.8823811 | Immunology | cd4 t | -0.2091647 | 0.0016475 | -0.2123936 | -0.2059357 | -5.1532389 | 0.0000003 | 0.0244775 | 0.0008217 | 218 |
| cd4 cd44+cd62l- nkt cells | Immunophenotyping | 686 | 685 | 6.9663110 | 0.2925099 | 0.3647916 | 0.2204968 | 6.6640062 | 0.2925543 | -0.1563776 | 0.2019070 | 0.3023048 | 0.0235275 | 0.0000000 | 0.5211691 | 0.2418983 | 0.0314222 | 0.3921028 | 0.4205739 | 0.1410986 | 0.9279631 | Immunology | cd4 nkt | -0.0700973 | 0.0014652 | -0.0729690 | -0.0672256 | -1.8312705 | 0.0672774 | 0.1420463 | 0.0007310 | 219 |
| cd4 cd44+cd62l- t cells | Immunophenotyping | 686 | 685 | 9.2626273 | 0.1914742 | -0.1907810 | 0.1537038 | 9.2431911 | 0.1915986 | -0.0065201 | 0.1496941 | 0.0194362 | 0.0176445 | 0.2709050 | -0.1842609 | 0.1796025 | 0.3051515 | 0.2759482 | 0.3344501 | 0.0231577 | 0.8920034 | Immunology | cd4 t | -0.1922757 | 0.0014652 | -0.1951475 | -0.1894040 | -5.0231443 | 0.0000006 | 0.0231619 | 0.0007310 | 220 |
| cd4 cd44+cd62l+ nkt cells | Immunophenotyping | 686 | 685 | 4.9246117 | 0.5303161 | 0.0647523 | 0.3157115 | 4.8650006 | 0.5302861 | -0.0813958 | 0.2935970 | 0.0596110 | 0.0217144 | 0.0061474 | 0.1461480 | 0.2247517 | 0.5156599 | 0.3894314 | 0.3571835 | 0.0216298 | 0.9646249 | Immunology | cd4 nkt | 0.0864369 | 0.0014652 | 0.0835652 | 0.0893087 | 2.2581384 | 0.0240939 | 0.0216332 | 0.0007310 | 221 |
| cd4 cd44+cd62l+ t cells | Immunophenotyping | 591 | 594 | 9.2160297 | 0.6288762 | -0.3675954 | 0.2435964 | 9.2286616 | 0.6289846 | -0.1475078 | 0.2404442 | -0.0126319 | 0.0227291 | 0.5785088 | -0.2200875 | 0.2398986 | 0.3591619 | 0.3267855 | 0.4112739 | 0.0227867 | 0.9623069 | Immunology | cd4 t | -0.2299510 | 0.0016964 | -0.2332758 | -0.2266261 | -5.5831039 | 0.0000000 | 0.0227906 | 0.0008460 | 222 |
| cd4 effector | FACS | 300 | 298 | 9.4316430 | 0.0321477 | 0.6370346 | 0.2429221 | 9.2515424 | 0.0395555 | 0.5356152 | 0.3635949 | 0.1801006 | 0.0386819 | 0.0000041 | 0.1014195 | 0.4303964 | 0.8138062 | 0.3184624 | 0.5095519 | 0.2149775 | 0.5258580 | Immunology | cd4 t | -0.4700387 | 0.0033784 | -0.4766603 | -0.4634171 | -8.0868050 | 0.0000000 | 0.2183842 | 0.0016807 | 223 |
| cd4 nkt cells | Immunophenotyping | 689 | 688 | 7.2548562 | 0.2497355 | 0.2674509 | 0.1982751 | 7.0025536 | 0.2497866 | -0.0497110 | 0.1825165 | 0.2523026 | 0.0213560 | 0.0000000 | 0.3171619 | 0.2197245 | 0.1491822 | 0.3558157 | 0.3852862 | 0.1402216 | 0.9133516 | Immunology | cd4 nkt | -0.0795747 | 0.0014588 | -0.0824339 | -0.0767155 | -2.0834289 | 0.0373965 | 0.1411516 | 0.0007278 | 224 |
| cd4 resting/naive | FACS | 300 | 298 | 10.1096399 | 0.0466003 | 0.1759675 | 0.3181521 | 10.0187065 | 0.0510264 | 0.2971414 | 0.4026180 | 0.0909335 | 0.0456029 | 0.0466874 | -0.1211738 | 0.5024880 | 0.8095387 | 0.4159304 | 0.5479444 | 0.0869220 | 0.5941274 | Immunology | cd4 t | -0.2756673 | 0.0033784 | -0.2822889 | -0.2690458 | -4.7427329 | 0.0000026 | 0.0871420 | 0.0016807 | 225 |
| cd4 t cells | Immunophenotyping | 689 | 688 | 10.6823656 | 0.1486596 | -0.1830921 | 0.1489516 | 10.6817799 | 0.1488449 | -0.0982620 | 0.1490543 | 0.0005857 | 0.0170226 | 0.9725569 | -0.0848301 | 0.1728135 | 0.6236133 | 0.2618108 | 0.3304283 | 0.0306437 | 0.8451081 | Immunology | cd4 t | -0.2327687 | 0.0014588 | -0.2356278 | -0.2299095 | -6.0943600 | 0.0000000 | 0.0306533 | 0.0007278 | 226 |
| cd4 t cells total | FACS | 300 | 298 | 10.8329935 | 0.0338298 | 0.3271719 | 0.2192313 | 10.7378153 | 0.0376070 | 0.4022147 | 0.2941659 | 0.0951782 | 0.0326587 | 0.0037230 | -0.0750428 | 0.3593399 | 0.8346613 | 0.2841590 | 0.4017522 | 0.1342687 | 0.6158130 | Immunology | cd4 t | -0.3463128 | 0.0033784 | -0.3529344 | -0.3396913 | -5.9581574 | 0.0000000 | 0.1350844 | 0.0016807 | 227 |
| cd44+ t-regs | Immunophenotyping | 74 | 77 | 6.3545697 | 0.3414293 | 0.7146108 | 1.0881441 | 6.4346331 | 0.3418406 | -0.1071702 | 1.0293303 | -0.0800633 | 0.0521752 | 0.1275370 | 0.8217810 | 0.8016419 | 0.3073675 | 0.2892229 | 0.3366362 | 0.0289092 | 0.9844958 | Immunology | cd44+ t-regs | -0.1515199 | 0.0137990 | -0.1785655 | -0.1244743 | -1.2898694 | 0.1990944 | 0.0289173 | 0.0067568 | 228 |
| cd62l+ t-regs | Immunophenotyping | 74 | 77 | 6.4863923 | 0.2750100 | -0.8179743 | 1.1078436 | 6.5627979 | 0.2751663 | -1.0830572 | 1.0355160 | -0.0764056 | 0.0511385 | 0.1377762 | 0.2650829 | 0.7923427 | 0.7385442 | 0.2965012 | 0.3182877 | 0.0568762 | 0.9768146 | Immunology | cd62l+ t-regs | -0.0706192 | 0.0137990 | -0.0976647 | -0.0435736 | -0.6011718 | 0.5486391 | 0.0569376 | 0.0067568 | 229 |
| cd8 cd25- nkt cells | Immunophenotyping | 610 | 606 | 5.9944422 | 0.4044507 | 0.2307349 | 0.2654053 | 6.0427342 | 0.4044030 | 0.2325884 | 0.2571268 | -0.0482920 | 0.0203414 | 0.0177905 | -0.0018534 | 0.2283999 | 0.9935270 | 0.3184846 | 0.3347689 | 0.0289525 | 0.9560683 | Immunology | cd8 nkt | -0.0498719 | 0.0016529 | -0.0531115 | -0.0466322 | -1.2266794 | 0.2201810 | 0.0289606 | 0.0008244 | 230 |
| cd8 cd25- t cells | Immunophenotyping | 612 | 608 | 10.3955118 | 0.1966100 | -0.0305775 | 0.1695677 | 10.3934481 | 0.1967942 | -0.1042379 | 0.1770227 | 0.0020638 | 0.0190436 | 0.9137244 | 0.0736604 | 0.2117792 | 0.7280544 | 0.2723856 | 0.3468972 | 0.0121314 | 0.8663783 | Immunology | cd8 t | -0.2418151 | 0.0016475 | -0.2450440 | -0.2385861 | -5.9576549 | 0.0000000 | 0.0121320 | 0.0008217 | 231 |
| cd8 cd25+ nkt cells | Immunophenotyping | 610 | 606 | 2.1349307 | 0.3134866 | 0.4316685 | 0.4405082 | 2.2485255 | 0.3133773 | 0.1489056 | 0.4115944 | -0.1135948 | 0.0338387 | 0.0008193 | 0.2827629 | 0.3821612 | 0.4595402 | 0.5634667 | 0.5293869 | 0.0507824 | 0.9008952 | Immunology | cd8 nkt | 0.0623832 | 0.0016529 | 0.0591435 | 0.0656228 | 1.5344147 | 0.1251882 | 0.0508261 | 0.0008244 | 232 |
| cd8 cd25+ t cells | Immunophenotyping | 612 | 608 | 3.7175563 | 1.0347245 | 0.0516371 | 0.2612579 | 3.8017266 | 1.0346797 | -0.2026598 | 0.2319906 | -0.0841703 | 0.0255916 | 0.0010423 | 0.2542969 | 0.2897786 | 0.3804055 | 0.4273067 | 0.3990809 | 0.0199652 | 0.9825993 | Immunology | cd8 t | 0.0683323 | 0.0016475 | 0.0651034 | 0.0715613 | 1.6835197 | 0.0925308 | 0.0199678 | 0.0008217 | 233 |
| cd8 cd44-cd62l- t cells | Immunophenotyping | 559 | 558 | 7.5921345 | 0.4742509 | -0.3281133 | 0.2744207 | 7.5365513 | 0.4743283 | -0.1120815 | 0.2656381 | 0.0555832 | 0.0294183 | 0.0591689 | -0.2160318 | 0.2900595 | 0.4566026 | 0.4132870 | 0.5054007 | 0.0288047 | 0.9432775 | Immunology | cd8 t | -0.2012110 | 0.0018002 | -0.2047393 | -0.1976827 | -4.7423494 | 0.0000024 | 0.0288126 | 0.0008977 | 234 |
| cd8 cd44-cd62l+ nkt cells | Immunophenotyping | 684 | 683 | 3.2196840 | 0.6674444 | -0.0686447 | 0.4516928 | 3.1444566 | 0.6675132 | -0.5866874 | 0.4444816 | 0.0752274 | 0.0277478 | 0.0068127 | 0.5180427 | 0.2829222 | 0.0673708 | 0.4337661 | 0.5164794 | 0.0323475 | 0.9647834 | Immunology | cd8 nkt | -0.1745311 | 0.0014695 | -0.1774113 | -0.1716509 | -4.5528856 | 0.0000058 | 0.0323588 | 0.0007331 | 235 |
| cd8 cd44-cd62l+ t cells | Immunophenotyping | 686 | 685 | 9.5554042 | 0.2871829 | -0.1202010 | 0.1962214 | 9.5314896 | 0.2872875 | -0.2665652 | 0.1930308 | 0.0239146 | 0.0191258 | 0.2114304 | 0.1463642 | 0.1945371 | 0.4519918 | 0.2891297 | 0.3647928 | 0.0268133 | 0.9419049 | Immunology | cd8 t | -0.2324551 | 0.0014652 | -0.2353268 | -0.2295833 | -6.0728179 | 0.0000000 | 0.0268197 | 0.0007310 | 236 |
| cd8 cd44+cd62l- nkt cells | Immunophenotyping | 684 | 683 | 3.8072073 | 0.2161393 | 0.4604029 | 0.2192639 | 3.8160088 | 0.2161963 | -0.0463071 | 0.1995757 | -0.0088015 | 0.0234699 | 0.7077240 | 0.5067100 | 0.2412446 | 0.0359262 | 0.3926891 | 0.4159989 | 0.0316719 | 0.9170435 | Immunology | cd8 nkt | -0.0576656 | 0.0014695 | -0.0605458 | -0.0547854 | -1.5042866 | 0.1327389 | 0.0316825 | 0.0007331 | 237 |
| cd8 cd44+cd62l- t cells | Immunophenotyping | 686 | 685 | 7.4006532 | 0.2415514 | 0.0100380 | 0.1845646 | 7.4301311 | 0.2416718 | 0.1001195 | 0.1777378 | -0.0294779 | 0.0208129 | 0.1569677 | -0.0900815 | 0.2122573 | 0.6713605 | 0.3295832 | 0.3905776 | 0.0192588 | 0.9028997 | Immunology | cd8 t | -0.1697990 | 0.0014652 | -0.1726707 | -0.1669272 | -4.4359460 | 0.0000099 | 0.0192612 | 0.0007310 | 238 |
| cd8 cd44+cd62l+ nkt cells | Immunophenotyping | 684 | 683 | 5.4979342 | 0.4153163 | 0.1523324 | 0.2933761 | 5.5827693 | 0.4153454 | -0.0419679 | 0.2780861 | -0.0848351 | 0.0207600 | 0.0000471 | 0.1943003 | 0.2134735 | 0.3629290 | 0.3497257 | 0.3613761 | 0.0355885 | 0.9573740 | Immunology | cd8 nkt | -0.0327712 | 0.0014695 | -0.0356514 | -0.0298910 | -0.8548830 | 0.3927659 | 0.0356036 | 0.0007331 | 239 |
| cd8 cd44+cd62l+ t cells | Immunophenotyping | 686 | 685 | 9.0141757 | 0.2198485 | 0.1614866 | 0.1719562 | 9.0667648 | 0.2199712 | 0.1663321 | 0.1680718 | -0.0525890 | 0.0185796 | 0.0047344 | -0.0048455 | 0.1892495 | 0.9795781 | 0.2892888 | 0.3520140 | 0.0407540 | 0.9089008 | Immunology | cd8 t | -0.1962464 | 0.0014652 | -0.1991181 | -0.1933746 | -5.1268767 | 0.0000003 | 0.0407766 | 0.0007310 | 240 |
| cd8 effector | FACS | 300 | 298 | 7.3179462 | 0.0484923 | 0.3113251 | 0.2792952 | 7.2710965 | 0.0528524 | 0.6060945 | 0.3774740 | 0.0468497 | 0.0419456 | 0.2645619 | -0.2947694 | 0.4592559 | 0.5212681 | 0.3585878 | 0.5120033 | 0.0854662 | 0.6710800 | Immunology | cd8 t | -0.3561690 | 0.0033784 | -0.3627906 | -0.3495474 | -6.1277278 | 0.0000000 | 0.0856752 | 0.0016807 | 241 |
| cd8 naive | FACS | 300 | 298 | 10.0401885 | 0.0594301 | 0.1374292 | 0.3930908 | 9.9437547 | 0.0656019 | 0.4130036 | 0.5138803 | 0.0964338 | 0.0575554 | 0.0944573 | -0.2755745 | 0.6335866 | 0.6637895 | 0.5113149 | 0.7005530 | 0.0767267 | 0.5929040 | Immunology | cd8 t | -0.3148958 | 0.0033784 | -0.3215173 | -0.3082742 | -5.4176404 | 0.0000001 | 0.0768778 | 0.0016807 | 242 |
| cd8 nkt cells | Immunophenotyping | 687 | 686 | 5.9313764 | 0.3233951 | 0.2263348 | 0.2826198 | 5.9948396 | 0.3234478 | -0.0475589 | 0.2710133 | -0.0634632 | 0.0192773 | 0.0010266 | 0.2738937 | 0.1981629 | 0.1672071 | 0.3183377 | 0.3434742 | 0.0346953 | 0.9458175 | Immunology | cd8 nkt | -0.0760002 | 0.0014631 | -0.0788677 | -0.0731326 | -1.9869352 | 0.0471283 | 0.0347092 | 0.0007299 | 243 |
| cd8 resting | FACS | 300 | 298 | 8.8617107 | 0.0510823 | 0.2074373 | 0.3540241 | 8.9291631 | 0.0552928 | 0.8319564 | 0.4337106 | -0.0674524 | 0.0497322 | 0.1756070 | -0.6245191 | 0.5480586 | 0.2550311 | 0.4644633 | 0.5884541 | 0.0881694 | 0.5863871 | Immunology | cd8 t | -0.2366277 | 0.0033784 | -0.2432493 | -0.2300062 | -4.0710744 | 0.0000531 | 0.0883989 | 0.0016807 | 244 |
| cd8 t cells | Immunophenotyping | 689 | 688 | 10.3517132 | 0.1679389 | -0.0674333 | 0.1498431 | 10.3465565 | 0.1680877 | -0.0986331 | 0.1480518 | 0.0051567 | 0.0171120 | 0.7632071 | 0.0311998 | 0.1740603 | 0.8577770 | 0.2665601 | 0.3288996 | 0.0161272 | 0.8657430 | Immunology | cd8 t | -0.2101538 | 0.0014588 | -0.2130130 | -0.2072947 | -5.5022581 | 0.0000000 | 0.0161286 | 0.0007278 | 245 |
| cd8 t cells total | FACS | 300 | 298 | 10.4489539 | 0.0352046 | 0.1713083 | 0.2362014 | 10.3702502 | 0.0428108 | 0.5552705 | 0.3732637 | 0.0787038 | 0.0392662 | 0.0455657 | -0.3839622 | 0.4343401 | 0.3771108 | 0.3042686 | 0.5210247 | 0.1105333 | 0.5370235 | Immunology | cd8 t | -0.5378979 | 0.0033784 | -0.5445195 | -0.5312763 | -9.2542928 | 0.0000000 | 0.1109868 | 0.0016807 | 246 |
| cdc cd11b type | FACS | 186 | 180 | 8.2670676 | 0.0777554 | 0.3519351 | 0.2758373 | 8.2760172 | 0.0807946 | 0.0329118 | 0.4014595 | -0.0089496 | 0.0434009 | 0.8367643 | 0.3190233 | 0.4803048 | 0.5070511 | 0.2736622 | 0.3910511 | 0.0366946 | 0.8497994 | Immunology | cdcs | -0.3570364 | 0.0055571 | -0.3679281 | -0.3461447 | -4.7894809 | 0.0000024 | 0.0367111 | 0.0027548 | 247 |
| cdc cd8a type | FACS | 186 | 180 | 8.5336862 | 0.1294118 | 0.2656074 | 0.4080950 | 8.4528976 | 0.1321240 | 0.0939698 | 0.5386878 | 0.0807886 | 0.0605900 | 0.1833942 | 0.1716376 | 0.6654381 | 0.7966313 | 0.4066400 | 0.5192869 | 0.0451303 | 0.8904096 | Immunology | cdcs | -0.2446209 | 0.0055571 | -0.2555126 | -0.2337291 | -3.2814773 | 0.0011325 | 0.0451610 | 0.0027548 | 248 |
| cdcs | Immunophenotyping | 749 | 752 | 8.7317962 | 0.2410299 | -0.1171262 | 0.1910960 | 8.8759521 | 0.2411318 | -0.2893300 | 0.1932478 | -0.1441559 | 0.0137834 | 0.0000000 | 0.1722038 | 0.1337085 | 0.1980308 | 0.2031515 | 0.2869255 | 0.0886847 | 0.9560095 | Immunology | cdcs | -0.3452681 | 0.0013378 | -0.3478901 | -0.3426460 | -9.4397639 | 0.0000000 | 0.0889183 | 0.0006676 | 249 |
| cdcs cd11b type | Immunophenotyping | 749 | 752 | 8.0244299 | 0.2740595 | -0.1359497 | 0.1637744 | 8.2256062 | 0.2741788 | -0.2211137 | 0.1676541 | -0.2011763 | 0.0150435 | 0.0000000 | 0.0851640 | 0.1461558 | 0.5602110 | 0.2219465 | 0.3141140 | 0.0975837 | 0.9654798 | Immunology | cdcs | -0.3473171 | 0.0013378 | -0.3499391 | -0.3446950 | -9.4957848 | 0.0000000 | 0.0978953 | 0.0006676 | 250 |
| dc total | FACS | 186 | 180 | 9.1459775 | 0.1008431 | 0.2930993 | 0.3126620 | 9.1048039 | 0.1035000 | 0.0396441 | 0.4403279 | 0.0411736 | 0.0482732 | 0.3943593 | 0.2534553 | 0.5321388 | 0.6342005 | 0.3101216 | 0.4265257 | 0.0381350 | 0.8854989 | Immunology | dc total | -0.3188007 | 0.0055571 | -0.3296924 | -0.3079090 | -4.2765663 | 0.0000243 | 0.0381535 | 0.0027548 | 251 |
| dn cd25- nkt cells | Immunophenotyping | 607 | 603 | 6.3274841 | 0.6095419 | 0.2817323 | 0.2196439 | 6.1874495 | 0.6095087 | -0.3285063 | 0.2156962 | 0.1400346 | 0.0201865 | 0.0000000 | 0.6102387 | 0.2259830 | 0.0070497 | 0.3009388 | 0.3433190 | 0.0453358 | 0.9813847 | Immunology | dn nkt | -0.1317588 | 0.0016611 | -0.1350146 | -0.1285030 | -3.2327741 | 0.0012591 | 0.0453669 | 0.0008285 | 252 |
| dn cd25- t cells | Immunophenotyping | 607 | 603 | 8.5276636 | 0.3104500 | 0.0383203 | 0.2562998 | 8.3060094 | 0.3105152 | -0.2644980 | 0.2640176 | 0.2216541 | 0.0193215 | 0.0000000 | 0.3028183 | 0.2132401 | 0.1559131 | 0.2663536 | 0.3510782 | 0.1341266 | 0.9287198 | Immunology | dn t | -0.2761897 | 0.0016611 | -0.2794455 | -0.2729340 | -6.7764681 | 0.0000000 | 0.1349397 | 0.0008285 | 253 |
| dn cd25+ nkt cells | Immunophenotyping | 607 | 603 | 2.3598764 | 0.4983643 | 0.0268145 | 0.3602305 | 2.3517458 | 0.4982734 | 0.0863283 | 0.3351459 | 0.0081306 | 0.0305187 | 0.7899784 | -0.0595139 | 0.3436135 | 0.8625312 | 0.4965404 | 0.4799809 | 0.0042607 | 0.9575244 | Immunology | dn nkt | 0.0339130 | 0.0016611 | 0.0306572 | 0.0371688 | 0.8320741 | 0.4055316 | 0.0042607 | 0.0008285 | 254 |
| dn cd25+ t cells | Immunophenotyping | 607 | 603 | 4.5632924 | 0.4761382 | 0.1572167 | 0.3049456 | 4.5332241 | 0.4761048 | -0.6888198 | 0.2976715 | 0.0300683 | 0.0278934 | 0.2813208 | 0.8460365 | 0.3121824 | 0.0068485 | 0.4223153 | 0.4709540 | 0.0349310 | 0.9511160 | Immunology | dn t | -0.1090137 | 0.0016611 | -0.1122695 | -0.1057579 | -2.6747114 | 0.0075803 | 0.0349452 | 0.0008285 | 255 |
| dn cd44-cd62l- t cells | Immunophenotyping | 554 | 553 | 5.9976332 | 0.2937064 | 0.1724716 | 0.2311398 | 5.7351797 | 0.2938437 | 0.0979205 | 0.2230596 | 0.2624536 | 0.0224011 | 0.0000000 | 0.0745511 | 0.2199063 | 0.7346824 | 0.3164163 | 0.3803399 | 0.1455563 | 0.9236334 | Immunology | dn t | -0.1840083 | 0.0018165 | -0.1875686 | -0.1804480 | -4.3173367 | 0.0000172 | 0.1465975 | 0.0009058 | 256 |
| dn cd44-cd62l+ nkt cells | Immunophenotyping | 681 | 680 | 1.8673704 | 0.4479738 | 0.5633420 | 0.3578919 | 1.5647239 | 0.4479701 | -0.0949913 | 0.3298259 | 0.3026465 | 0.0331977 | 0.0000000 | 0.6583333 | 0.3403706 | 0.0533561 | 0.5628688 | 0.5811658 | 0.1073969 | 0.9197530 | Immunology | dn nkt | -0.0319906 | 0.0014760 | -0.0348836 | -0.0290977 | -0.8326784 | 0.4051724 | 0.1078127 | 0.0007364 | 257 |
| dn cd44-cd62l+ t cells | Immunophenotyping | 681 | 680 | 6.5512251 | 0.2813676 | 0.0339278 | 0.1888114 | 6.2548262 | 0.2814596 | -0.3983291 | 0.1843137 | 0.2963989 | 0.0194092 | 0.0000000 | 0.4322569 | 0.1969138 | 0.0283665 | 0.2982262 | 0.3664318 | 0.1683258 | 0.9288651 | Immunology | dn t | -0.2059612 | 0.0014760 | -0.2088542 | -0.2030683 | -5.3609287 | 0.0000001 | 0.1699431 | 0.0007364 | 258 |
| dn cd44+cd62l- nkt cells | Immunophenotyping | 681 | 680 | 6.0711243 | 0.3034181 | 0.3040535 | 0.1818725 | 5.8776205 | 0.3034801 | -0.3387792 | 0.1706880 | 0.1935038 | 0.0199408 | 0.0000000 | 0.6428327 | 0.2037768 | 0.0016520 | 0.3193968 | 0.3639725 | 0.0940737 | 0.9494554 | Immunology | dn nkt | -0.1306453 | 0.0014760 | -0.1335382 | -0.1277524 | -3.4005431 | 0.0006920 | 0.0943527 | 0.0007364 | 259 |
| dn cd44+cd62l- t cells | Immunophenotyping | 681 | 680 | 6.9418802 | 0.4906665 | -0.0266296 | 0.2130669 | 6.7315490 | 0.4906983 | 0.1752124 | 0.2042182 | 0.2103312 | 0.0188518 | 0.0000000 | -0.2018420 | 0.1921100 | 0.2936525 | 0.2991596 | 0.3455970 | 0.0805729 | 0.9696383 | Immunology | dn t | -0.1442973 | 0.0014760 | -0.1471902 | -0.1414044 | -3.7558891 | 0.0001800 | 0.0807480 | 0.0007364 | 260 |
| dn cd44+cd62l+ nkt cells | Immunophenotyping | 681 | 680 | 5.6833879 | 0.4543767 | 0.1844975 | 0.2796651 | 5.6420180 | 0.4544017 | -0.4625080 | 0.2625634 | 0.0413699 | 0.0211585 | 0.0508151 | 0.6470055 | 0.2173414 | 0.0029772 | 0.3575508 | 0.3651679 | 0.0328279 | 0.9618280 | Immunology | dn nkt | -0.0210809 | 0.0014760 | -0.0239738 | -0.0181880 | -0.5487112 | 0.5832938 | 0.0328397 | 0.0007364 | 261 |
| dn cd44+cd62l+ t cells | Immunophenotyping | 681 | 680 | 7.2491570 | 0.4596846 | -0.0588866 | 0.2306806 | 7.0560710 | 0.4597473 | -0.3062278 | 0.2263593 | 0.1930860 | 0.0186952 | 0.0000000 | 0.2473411 | 0.1896155 | 0.1923656 | 0.2860639 | 0.3521944 | 0.0823507 | 0.9641514 | Immunology | dn t | -0.2079692 | 0.0014760 | -0.2108622 | -0.2050763 | -5.4131946 | 0.0000001 | 0.0825377 | 0.0007364 | 262 |
| dn nkt cells | Immunophenotyping | 745 | 744 | 6.6675202 | 0.3184467 | 0.1774424 | 0.1884334 | 6.5406151 | 0.3184810 | -0.3325429 | 0.1802701 | 0.1269051 | 0.0174747 | 0.0000000 | 0.5099854 | 0.1724433 | 0.0031644 | 0.2926806 | 0.3349632 | 0.0687354 | 0.9517473 | Immunology | dn nkt | -0.1349396 | 0.0013486 | -0.1375828 | -0.1322964 | -3.6744717 | 0.0002468 | 0.0688439 | 0.0006729 | 263 |
| dn t cells | Immunophenotyping | 687 | 686 | 8.5792220 | 0.2401682 | 0.0184272 | 0.2072807 | 8.3626982 | 0.2402706 | -0.2109305 | 0.2032337 | 0.2165238 | 0.0171842 | 0.0000000 | 0.2293577 | 0.1748774 | 0.1899561 | 0.2648616 | 0.3259104 | 0.1426389 | 0.9224992 | Immunology | dn t | -0.2074163 | 0.0014631 | -0.2102838 | -0.2045487 | -5.4226544 | 0.0000001 | 0.1436183 | 0.0007299 | 264 |
| dp t cells | Immunophenotyping | 562 | 561 | 5.8539196 | 0.7820114 | -0.2290863 | 0.2764762 | 5.8451178 | 0.7819974 | -0.1171045 | 0.2639105 | 0.0088018 | 0.0264306 | 0.7392029 | -0.1119818 | 0.2627428 | 0.6700643 | 0.3850724 | 0.4437822 | 0.0106182 | 0.9740189 | Immunology | dp t | -0.1419041 | 0.0017905 | -0.1454134 | -0.1383948 | -3.3535622 | 0.0008245 | 0.0106186 | 0.0008929 | 265 |
| follicular b cells | Immunophenotyping | 381 | 377 | 12.1646813 | 0.2519434 | 0.1143153 | 0.3456427 | 12.1738350 | 0.2524276 | -0.2132394 | 0.3568163 | -0.0091537 | 0.0223476 | 0.6822376 | 0.3275547 | 0.2187676 | 0.1348396 | 0.2108714 | 0.3623794 | 0.0292074 | 0.8951460 | Immunology | follicular b cells | -0.5414573 | 0.0026596 | -0.5466701 | -0.5362445 | -10.4990976 | 0.0000000 | 0.0292157 | 0.0013245 | 266 |
| follicular b cells (cd21/35+) | Immunophenotyping | 452 | 454 | 11.9909085 | 0.3157436 | 0.2497074 | 0.2562952 | 11.9998643 | 0.3159322 | -0.0031313 | 0.2767775 | -0.0089558 | 0.0177575 | 0.6141756 | 0.2528387 | 0.2286373 | 0.2691601 | 0.1825791 | 0.3052022 | 0.0216299 | 0.9337700 | Immunology | follicular b cells | -0.5137863 | 0.0022222 | -0.5181418 | -0.5094308 | -10.8990255 | 0.0000000 | 0.0216333 | 0.0011074 | 267 |
| gd + b1 | FACS | 300 | 298 | 9.4302130 | 0.0481783 | 0.2756359 | 0.2037896 | 9.2257307 | 0.0492374 | 0.2833102 | 0.2391179 | 0.2044823 | 0.0281853 | 0.0000000 | -0.0076743 | 0.3056320 | 0.9799775 | 0.2613835 | 0.3139393 | 0.2018015 | 0.8369350 | Immunology | B cells | -0.1832224 | 0.0033784 | -0.1898439 | -0.1766008 | -3.1522588 | 0.0017015 | 0.2046098 | 0.0016807 | 268 |
| gd t cells | Immunophenotyping | 74 | 77 | 7.0508320 | 0.1073987 | -0.2276559 | 0.8981046 | 6.7357486 | 0.1067556 | -0.8380664 | 0.8275060 | 0.3150835 | 0.0392720 | 0.0000000 | 0.6104105 | 0.6117984 | 0.3204173 | 0.2442430 | 0.2287532 | 0.2989221 | 0.9046699 | Immunology | t cells | 0.0658058 | 0.0137990 | 0.0387603 | 0.0928514 | 0.5601964 | 0.5761867 | 0.3083355 | 0.0067568 | 269 |
| inkt | FACS | 300 | 298 | 8.0804676 | 0.0555942 | 0.3706836 | 0.2112767 | 7.7058380 | 0.0563098 | 0.0079188 | 0.2382613 | 0.3746297 | 0.0285907 | 0.0000000 | 0.3627648 | 0.3094179 | 0.2415861 | 0.2713833 | 0.3101684 | 0.3108020 | 0.8794460 | Immunology | inkt | -0.1335945 | 0.0033784 | -0.1402161 | -0.1269729 | -2.2984335 | 0.0218824 | 0.3214329 | 0.0016807 | 270 |
| klrg1+ cd4 t cells | Immunophenotyping | 74 | 77 | 6.7829709 | 0.1191211 | 0.8189534 | 0.9925853 | 6.5915805 | 0.1190254 | 0.0936251 | 0.9246307 | 0.1913904 | 0.0450018 | 0.0000420 | 0.7253283 | 0.6955277 | 0.2991160 | 0.2684393 | 0.2740165 | 0.1714029 | 0.8927175 | Immunology | klrg1 | -0.0202780 | 0.0137990 | -0.0473236 | 0.0067676 | -0.1726241 | 0.8631812 | 0.1731117 | 0.0067568 | 271 |
| klrg1+ cd4+ nkt cells | Immunophenotyping | 74 | 77 | 3.8470873 | 0.1906116 | 1.2716572 | 1.4089029 | 3.6515456 | 0.1896682 | -0.1284708 | 1.2958237 | 0.1955417 | 0.0613511 | 0.0018315 | 1.4001279 | 0.9586850 | 0.1467750 | 0.3825910 | 0.3557319 | 0.1193669 | 0.9242639 | Immunology | klrg1 | 0.0730749 | 0.0137990 | 0.0460293 | 0.1001204 | 0.6220769 | 0.5348420 | 0.1199387 | 0.0067568 | 272 |
| klrg1+ t-regs | Immunophenotyping | 74 | 77 | 4.7548111 | 0.2975055 | 0.0140274 | 1.4797495 | 4.7721733 | 0.2970779 | -0.3961315 | 1.3649584 | -0.0173622 | 0.0653181 | 0.7908419 | 0.4101589 | 1.0213837 | 0.6887134 | 0.3994343 | 0.3862354 | 0.0163867 | 0.9668366 | Immunology | klrg1 | 0.0338878 | 0.0137990 | 0.0068423 | 0.0609334 | 0.2884826 | 0.7733783 | 0.0163882 | 0.0067568 | 273 |
| macrophages | FACS | 299 | 294 | 7.2451691 | 0.1030266 | 0.1988204 | 0.2519532 | 7.0962927 | 0.1047795 | 0.4139148 | 0.3498563 | 0.1488765 | 0.0388085 | 0.0001410 | -0.2150944 | 0.4190392 | 0.6079660 | 0.3142346 | 0.4619202 | 0.0754934 | 0.9270329 | Immunology | macrophages | -0.3852814 | 0.0034074 | -0.3919598 | -0.3786030 | -6.6003399 | 0.0000000 | 0.0756374 | 0.0016949 | 274 |
| mzb | Immunophenotyping | 138 | 140 | 10.0057267 | 0.1064813 | -0.6746870 | 1.2056066 | 10.0396491 | 0.1043051 | 0.2142750 | 1.1306893 | -0.0339225 | 0.0369349 | 0.3593730 | -0.8889620 | 0.5724315 | 0.1218386 | 0.3531930 | 0.2424778 | 0.0595274 | 0.8491211 | Immunology | mzb | 0.3761584 | 0.0073533 | 0.3617461 | 0.3905707 | 4.3866046 | 0.0000164 | 0.0595979 | 0.0036364 | 275 |
| mzb (cd21/35 high) | Immunophenotyping | 450 | 452 | 9.7246904 | 0.6625035 | 0.0840921 | 0.3629877 | 9.7498306 | 0.6625083 | -0.1614775 | 0.3173037 | -0.0251402 | 0.0276139 | 0.3629082 | 0.2455696 | 0.3750059 | 0.5127793 | 0.4022572 | 0.3806625 | 0.0140660 | 0.9438019 | Immunology | mzb | 0.0551836 | 0.0022322 | 0.0508087 | 0.0595586 | 1.1680143 | 0.2431103 | 0.0140669 | 0.0011123 | 276 |
| nk cells (panel a) | Immunophenotyping | 750 | 749 | 9.1726944 | 0.2156567 | -0.1288677 | 0.2075278 | 9.2412981 | 0.2157065 | -0.1512984 | 0.2012777 | -0.0686037 | 0.0155514 | 0.0000112 | 0.0224307 | 0.1535152 | 0.8838564 | 0.2666116 | 0.2978052 | 0.0564308 | 0.9040358 | Immunology | nk cells | -0.1106474 | 0.0013396 | -0.1132730 | -0.1080219 | -3.0231254 | 0.0025443 | 0.0564908 | 0.0006684 | 277 |
| nk cells (panel b) | Immunophenotyping | 680 | 678 | 8.9791475 | 0.1431487 | -0.0944147 | 0.2459994 | 9.0074450 | 0.1433488 | -0.2129947 | 0.2405613 | -0.0282975 | 0.0177677 | 0.1115310 | 0.1185801 | 0.1771859 | 0.5034834 | 0.2770597 | 0.3354135 | 0.0424152 | 0.8180227 | Immunology | nk cells | -0.1911333 | 0.0014793 | -0.1940327 | -0.1882340 | -4.9694607 | 0.0000008 | 0.0424407 | 0.0007380 | 278 |
| nk klrg1+ cells | Immunophenotyping | 74 | 77 | 6.8804386 | 0.1855338 | 1.2731957 | 0.9919111 | 6.7684762 | 0.1852282 | -0.6113552 | 0.9152413 | 0.1119624 | 0.0438089 | 0.0118464 | 1.8845509 | 0.6846664 | 0.0068330 | 0.2678451 | 0.2591797 | 0.0919137 | 0.9616203 | Immunology | nk cells | 0.0331727 | 0.0137990 | 0.0061272 | 0.0602183 | 0.2823952 | 0.7780324 | 0.0921738 | 0.0067568 | 279 |
| nk subsets (q1) | Immunophenotyping | 603 | 602 | 6.6222422 | 0.3761067 | -0.0025530 | 0.2437180 | 6.6505753 | 0.3761145 | -0.2332910 | 0.2499791 | -0.0283331 | 0.0228335 | 0.2149659 | 0.2307380 | 0.2529744 | 0.3619494 | 0.3303102 | 0.4065144 | 0.0236112 | 0.9223617 | Immunology | nk cells | -0.2075884 | 0.0016681 | -0.2108577 | -0.2043191 | -5.0827357 | 0.0000004 | 0.0236156 | 0.0008319 | 280 |
| nk subsets (q2) | Immunophenotyping | 603 | 602 | 7.4643625 | 0.3199905 | -0.1950168 | 0.2842230 | 7.3772087 | 0.3201704 | -0.0519481 | 0.2987706 | 0.0871538 | 0.0211080 | 0.0000397 | -0.1430688 | 0.2314249 | 0.5365876 | 0.2667532 | 0.3931124 | 0.0444505 | 0.9486540 | Immunology | nk cells | -0.3877730 | 0.0016681 | -0.3910423 | -0.3845037 | -9.4944981 | 0.0000000 | 0.0444798 | 0.0008319 | 281 |
| nk subsets (q3) | Immunophenotyping | 537 | 533 | 7.4624563 | 0.2537036 | 0.2443445 | 0.2194458 | 7.6417580 | 0.2539612 | -0.5021930 | 0.2395310 | -0.1793018 | 0.0210186 | 0.0000000 | 0.7465375 | 0.2579018 | 0.0038923 | 0.2557127 | 0.3724452 | 0.1329056 | 0.8945234 | Immunology | nk cells | -0.3760426 | 0.0018797 | -0.3797268 | -0.3723584 | -8.6734081 | 0.0000000 | 0.1336965 | 0.0009372 | 282 |
| nk subsets (q4) | Immunophenotyping | 537 | 533 | 7.7482173 | 0.0716934 | -0.3977566 | 0.3329521 | 7.7615655 | 0.0728855 | -0.1580208 | 0.3507710 | -0.0133483 | 0.0229221 | 0.5604977 | -0.2397358 | 0.2785645 | 0.3896932 | 0.2745878 | 0.4089080 | 0.0558882 | 0.7051965 | Immunology | nk cells | -0.3982260 | 0.0018797 | -0.4019102 | -0.3945418 | -9.1850685 | 0.0000000 | 0.0559465 | 0.0009372 | 283 |
| nk total | FACS | 300 | 298 | 9.7132913 | 0.0497660 | -0.3794602 | 0.2392735 | 9.7727219 | 0.0544223 | 0.0937370 | 0.3605391 | -0.0594306 | 0.0388675 | 0.1268777 | -0.4731972 | 0.4235058 | 0.2643841 | 0.3015653 | 0.4912944 | 0.0560026 | 0.7182654 | Immunology | nkt cells | -0.4880684 | 0.0033784 | -0.4946900 | -0.4814468 | -8.3969987 | 0.0000000 | 0.0560613 | 0.0016807 | 284 |
| nkt cells (panel a) | Immunophenotyping | 750 | 749 | 8.0260703 | 0.2565680 | 0.1402757 | 0.1761779 | 7.8800481 | 0.2565899 | -0.0231144 | 0.1655260 | 0.1460222 | 0.0170500 | 0.0000000 | 0.1633900 | 0.1690282 | 0.3339196 | 0.2950648 | 0.3229593 | 0.0882648 | 0.9294212 | Immunology | nkt cells | -0.0903323 | 0.0013396 | -0.0929578 | -0.0877068 | -2.4680725 | 0.0136954 | 0.0884951 | 0.0006684 | 285 |
| nkt cells (panel b) | Immunophenotyping | 674 | 672 | 7.4030851 | 0.4702657 | 0.1868061 | 0.1962373 | 7.2283082 | 0.4703380 | -0.0191105 | 0.1862906 | 0.1747770 | 0.0209812 | 0.0000000 | 0.2059166 | 0.2105471 | 0.3282895 | 0.3262882 | 0.3905208 | 0.0766841 | 0.9494507 | Immunology | nkt cells | -0.1797022 | 0.0014925 | -0.1826275 | -0.1767769 | -4.6514711 | 0.0000036 | 0.0768350 | 0.0007446 | 286 |
| nkt dn klrg1+ cells | Immunophenotyping | 74 | 77 | 4.5333542 | 0.1905187 | 0.6899704 | 1.3158080 | 4.5172575 | 0.1919903 | 0.1550541 | 1.2555600 | 0.0160967 | 0.0644019 | 0.8030602 | 0.5349163 | 0.9805162 | 0.5863910 | 0.3506541 | 0.4222358 | 0.0316386 | 0.9155278 | Immunology | nkt cells | -0.1854783 | 0.0137990 | -0.2125238 | -0.1584327 | -1.5789526 | 0.1164676 | 0.0316492 | 0.0067568 | 287 |
| nkt effector | FACS | 300 | 298 | 8.3385036 | 0.0548333 | 0.3630269 | 0.2441887 | 7.9624184 | 0.0571290 | 0.1575144 | 0.3141740 | 0.3760851 | 0.0357093 | 0.0000000 | 0.2055125 | 0.3878692 | 0.5964492 | 0.3109454 | 0.4184540 | 0.3004721 | 0.8183404 | Immunology | nkt cells | -0.2969610 | 0.0033784 | -0.3035826 | -0.2903395 | -5.1090819 | 0.0000004 | 0.3100385 | 0.0016807 | 288 |
| nkt resting | FACS | 300 | 298 | 7.8689560 | 0.0597363 | -0.1010485 | 0.2517293 | 7.7148717 | 0.0604102 | 0.0111219 | 0.2743772 | 0.1540843 | 0.0333975 | 0.0000050 | -0.1121704 | 0.3618442 | 0.7566918 | 0.3252153 | 0.3563717 | 0.1233032 | 0.8432387 | Immunology | nkt cells | -0.0914983 | 0.0033784 | -0.0981198 | -0.0848767 | -1.5741865 | 0.1159750 | 0.1239339 | 0.0016807 | 289 |
| nkt subsets (q1) | Immunophenotyping | 603 | 602 | 6.1462004 | 0.3539421 | -0.0183124 | 0.1636842 | 6.0321626 | 0.3540193 | -0.2012370 | 0.1668643 | 0.1140378 | 0.0191622 | 0.0000000 | 0.1829246 | 0.2139021 | 0.3926695 | 0.2765886 | 0.3367763 | 0.0629559 | 0.9441366 | Immunology | nkt cells | -0.1968893 | 0.0016681 | -0.2001586 | -0.1936199 | -4.8207711 | 0.0000016 | 0.0630393 | 0.0008319 | 290 |
| nkt subsets (q3) | Immunophenotyping | 537 | 533 | 5.7911791 | 0.4935441 | 0.4667821 | 0.2459928 | 5.5644515 | 0.4937098 | -0.1621166 | 0.2599836 | 0.2267276 | 0.0278441 | 0.0000000 | 0.6288987 | 0.3458645 | 0.0693622 | 0.3597133 | 0.4739644 | 0.1038137 | 0.9285998 | Immunology | nkt cells | -0.2758320 | 0.0018797 | -0.2795161 | -0.2721478 | -6.3620535 | 0.0000000 | 0.1041891 | 0.0009372 | 291 |
| nkt total | FACS | 300 | 298 | 8.8575891 | 0.0547607 | 0.1968841 | 0.2124302 | 8.5657153 | 0.0558071 | 0.0141196 | 0.2506923 | 0.2918738 | 0.0295059 | 0.0000000 | 0.1827644 | 0.3195310 | 0.5675925 | 0.2717274 | 0.3286266 | 0.2481409 | 0.8634428 | Immunology | nkt cells | -0.1901341 | 0.0033784 | -0.1967556 | -0.1835125 | -3.2711714 | 0.0011329 | 0.2534307 | 0.0016807 | 292 |
| number of live cells acquired panel 1 | FACS | 308 | 305 | 12.4800620 | 0.0458743 | -0.2610088 | 0.4059821 | 12.4431714 | 0.0496780 | 0.5208886 | 0.4644385 | 0.0368906 | 0.0540891 | 0.4955228 | -0.7818974 | 0.6070411 | 0.1983047 | 0.5621636 | 0.6485364 | 0.0581656 | 0.4338133 | Immunology | number of live cells acquired panel 1 | -0.1429415 | 0.0032950 | -0.1493995 | -0.1364835 | -2.4901892 | 0.0130321 | 0.0582313 | 0.0016393 | 293 |
| number of live cells acquired panel 2 | FACS | 308 | 304 | 12.6187509 | 0.0596764 | -0.0763443 | 0.3974476 | 12.5190013 | 0.0670498 | 0.4686869 | 0.5440429 | 0.0997496 | 0.0597945 | 0.0958798 | -0.5450312 | 0.6603506 | 0.4095443 | 0.5269835 | 0.7513915 | 0.0707193 | 0.5975417 | Immunology | number of live cells acquired panel 2 | -0.3547793 | 0.0033005 | -0.3612481 | -0.3483105 | -6.1754716 | 0.0000000 | 0.0708375 | 0.0016420 | 294 |
| others | Immunophenotyping | 750 | 749 | 12.2294604 | 0.1663371 | 0.0462253 | 0.1635948 | 12.2782460 | 0.1664687 | -0.0417610 | 0.1645028 | -0.0487856 | 0.0143589 | 0.0007025 | 0.0879863 | 0.1402601 | 0.5305784 | 0.2262692 | 0.2947414 | 0.0449135 | 0.8752070 | Immunology | others | -0.2643738 | 0.0013396 | -0.2669993 | -0.2617482 | -7.2232589 | 0.0000000 | 0.0449437 | 0.0006684 | 295 |
| pdcs | Immunophenotyping | 805 | 803 | 7.3168668 | 0.3962058 | 0.2442315 | 0.1993290 | 7.2434943 | 0.3961452 | 0.1798992 | 0.1869373 | 0.0733725 | 0.0205626 | 0.0003726 | 0.0643322 | 0.1946152 | 0.7410297 | 0.3661810 | 0.3844917 | 0.0360908 | 0.9499239 | Immunology | pdcs | -0.0487959 | 0.0012484 | -0.0512428 | -0.0463490 | -1.3810182 | 0.1674655 | 0.0361065 | 0.0006231 | 296 |
| percentage of live gated events in panel a | Immunophenotyping | 726 | 726 | 4.2193684 | 0.1595216 | -0.1381630 | 0.0355329 | 4.2547811 | 0.1595201 | -0.0105269 | 0.0325951 | -0.0354128 | 0.0032982 | 0.0000000 | -0.1276361 | 0.0330937 | 0.0001213 | 0.0598594 | 0.0573608 | 0.0517393 | 0.9886613 | Immunology | percentage of live gated events | 0.0426379 | 0.0013831 | 0.0399271 | 0.0453488 | 1.1464773 | 0.2517869 | 0.0517855 | 0.0006901 | 297 |
| percentage of live gated events in panel b | Immunophenotyping | 726 | 731 | 4.2123148 | 0.1504644 | -0.1112239 | 0.0298449 | 4.2412029 | 0.1504644 | -0.0096202 | 0.0271668 | -0.0288881 | 0.0032990 | 0.0000000 | -0.1016037 | 0.0328631 | 0.0020379 | 0.0584761 | 0.0591716 | 0.0454109 | 0.9867500 | Immunology | percentage of live gated events | -0.0118180 | 0.0013784 | -0.0145196 | -0.0091164 | -0.3183174 | 0.7502899 | 0.0454422 | 0.0006878 | 298 |
| rp macrophage (cd19- cd11c-) | Immunophenotyping | 611 | 612 | 8.0724703 | 0.4523823 | 0.2819547 | 0.2415953 | 7.9945802 | 0.4524220 | 0.2582230 | 0.2514542 | 0.0778901 | 0.0176241 | 0.0000110 | 0.0237317 | 0.1940819 | 0.9027054 | 0.2310374 | 0.3274515 | 0.0397145 | 0.9698190 | Immunology | rp macrophage (cd19- cd11c-) | -0.3487590 | 0.0016434 | -0.3519800 | -0.3455380 | -8.6031119 | 0.0000000 | 0.0397354 | 0.0008197 | 299 |
| rp macrophage (f4/80+) | Immunophenotyping | 233 | 231 | 8.6294361 | 0.0928740 | 0.1100659 | 0.2729603 | 8.7159951 | 0.0926704 | 0.2756587 | 0.2217734 | -0.0865590 | 0.0247584 | 0.0005282 | -0.1655928 | 0.2146382 | 0.4408955 | 0.2770841 | 0.2057292 | 0.0845023 | 0.9237071 | Immunology | rp macrophage (f4/80+) | 0.2977411 | 0.0043669 | 0.2891821 | 0.3063000 | 4.5055967 | 0.0000084 | 0.0847043 | 0.0021692 | 300 |
| t cells (panel a) | Immunophenotyping | 689 | 688 | 11.3374214 | 0.1592735 | -0.1145271 | 0.1440380 | 11.3166752 | 0.1594357 | -0.0973007 | 0.1438919 | 0.0207463 | 0.0165773 | 0.2110288 | -0.0172265 | 0.1683455 | 0.9185152 | 0.2550904 | 0.3213861 | 0.0312801 | 0.8613453 | Immunology | t cells | -0.2310261 | 0.0014588 | -0.2338853 | -0.2281670 | -6.0487374 | 0.0000000 | 0.0312903 | 0.0007278 | 301 |
| t cells (panel b) | Immunophenotyping | 746 | 748 | 11.3042561 | 0.0957862 | -0.2915950 | 0.1445435 | 11.2684128 | 0.0960255 | -0.1663277 | 0.1424845 | 0.0358432 | 0.0157596 | 0.0231227 | -0.1252673 | 0.1540296 | 0.4162286 | 0.2534210 | 0.3152409 | 0.0560750 | 0.8620572 | Immunology | t cells | -0.2182830 | 0.0013441 | -0.2209174 | -0.2156486 | -5.9539609 | 0.0000000 | 0.0561339 | 0.0006707 | 302 |
| t subset | Immunophenotyping | 603 | 603 | 10.2444294 | 0.1513234 | -0.1829492 | 0.1897863 | 10.3489559 | 0.1517182 | -0.0460139 | 0.2176044 | -0.1045265 | 0.0182814 | 0.0000000 | -0.1369353 | 0.1979868 | 0.4893347 | 0.2047987 | 0.3608714 | 0.0791892 | 0.8948085 | Immunology | t cells | -0.5664943 | 0.0016667 | -0.5697609 | -0.5632277 | -13.8762194 | 0.0000000 | 0.0793554 | 0.0008313 | 303 |
| t/nkt/b1 | FACS | 300 | 298 | 11.5045240 | 0.0341220 | 0.2649184 | 0.2116947 | 11.4047832 | 0.0377991 | 0.4218308 | 0.2878346 | 0.0997408 | 0.0318507 | 0.0018401 | -0.1569124 | 0.3498327 | 0.6539595 | 0.2731480 | 0.3925544 | 0.1386576 | 0.6353286 | Immunology | nkt cells | -0.3626727 | 0.0033784 | -0.3692943 | -0.3560511 | -6.2396211 | 0.0000000 | 0.1395566 | 0.0016807 | 304 |
| total cell count in spleen | FACS | 296 | 289 | 18.6110465 | 0.0214234 | 0.6040158 | 0.1552912 | 18.5172426 | 0.0206132 | 0.5909398 | 0.1414536 | 0.0938039 | 0.0189467 | 0.0000010 | 0.0130760 | 0.2053838 | 0.9492617 | 0.2117467 | 0.1829534 | 0.2675409 | 0.6454046 | Immunology | total cell count in spleen | 0.1461176 | 0.0034547 | 0.1393464 | 0.1528887 | 2.4859651 | 0.0131991 | 0.2742132 | 0.0017182 | 305 |
| total number of acquired events in panel a | Immunophenotyping | 748 | 748 | 12.9669211 | 0.1990172 | 0.0772400 | 0.1416961 | 12.9525066 | 0.1992780 | -0.0991741 | 0.1567511 | 0.0144145 | 0.0166843 | 0.3877856 | 0.1764141 | 0.1607587 | 0.2726969 | 0.2301054 | 0.3668314 | 0.0186820 | 0.8819438 | Immunology | number events | -0.4663648 | 0.0013423 | -0.4689956 | -0.4637340 | -12.7292824 | 0.0000000 | 0.0186842 | 0.0006698 | 306 |
| total number of acquired events in panel b | Immunophenotyping | 751 | 755 | 13.1487171 | 0.1940396 | 0.1226931 | 0.1239066 | 13.1050975 | 0.1945809 | -0.0330146 | 0.1745384 | 0.0436196 | 0.0176403 | 0.0135477 | 0.1557078 | 0.1626553 | 0.3386171 | 0.1518397 | 0.4357453 | 0.0406176 | 0.8737619 | Immunology | number events | -1.0542292 | 0.0013333 | -1.0568425 | -1.0516159 | -28.8711520 | 0.0000000 | 0.0406399 | 0.0006653 | 307 |
| transitional b cells | Immunophenotyping | 138 | 140 | 9.4810454 | 0.1114798 | 0.4425829 | 1.2568049 | 9.7843139 | 0.1092771 | -0.3619048 | 1.1792401 | -0.3032685 | 0.0386515 | 0.0000000 | 0.8044877 | 0.5988033 | 0.1804660 | 0.3680383 | 0.2559489 | 0.2601302 | 0.8491765 | Immunology | B cells | 0.3632633 | 0.0073533 | 0.3488510 | 0.3776756 | 4.2362271 | 0.0000310 | 0.2662480 | 0.0036364 | 308 |
| transitional b cells (cd21/35 low) | Immunophenotyping | 452 | 454 | 9.4913426 | 0.4395856 | 0.2947617 | 0.2430283 | 9.5566744 | 0.4397031 | -0.0702887 | 0.2482103 | -0.0653318 | 0.0225346 | 0.0038551 | 0.3650504 | 0.2989975 | 0.2225175 | 0.2706234 | 0.3577025 | 0.0325746 | 0.9584347 | Immunology | B cells | -0.2789683 | 0.0022222 | -0.2833238 | -0.2746128 | -5.9177973 | 0.0000000 | 0.0325861 | 0.0011074 | 309 |
| tregs | FACS | 300 | 298 | 8.4702394 | 0.0388709 | 0.5118581 | 0.2082048 | 8.3831186 | 0.0408984 | 0.4373272 | 0.2593067 | 0.0871207 | 0.0297402 | 0.0035501 | 0.0745310 | 0.3244577 | 0.8184108 | 0.2679443 | 0.3465087 | 0.1358444 | 0.7275501 | Immunology | tregs | -0.2571403 | 0.0033784 | -0.2637619 | -0.2505188 | -4.4239845 | 0.0000115 | 0.1366894 | 0.0016807 | 310 |
| tregs effector | FACS | 300 | 298 | 7.6714682 | 0.0404911 | 0.6355281 | 0.2243086 | 7.5857271 | 0.0454833 | 0.5410037 | 0.3363737 | 0.0857411 | 0.0361803 | 0.0181719 | 0.0945245 | 0.3961540 | 0.8115086 | 0.2850892 | 0.4611739 | 0.1372418 | 0.6562695 | Immunology | tregs | -0.4809846 | 0.0033784 | -0.4876062 | -0.4743630 | -8.2751244 | 0.0000000 | 0.1381133 | 0.0016807 | 311 |
| tregs resting | FACS | 300 | 298 | 7.8154214 | 0.0478884 | 0.4943295 | 0.2756468 | 7.7297579 | 0.0509230 | 0.5179710 | 0.3460155 | 0.0856636 | 0.0395032 | 0.0305860 | -0.0236415 | 0.4320362 | 0.9563824 | 0.3559238 | 0.4647010 | 0.1102073 | 0.6881667 | Immunology | tregs | -0.2666888 | 0.0033784 | -0.2733104 | -0.2600673 | -4.5882622 | 0.0000055 | 0.1106568 | 0.0016807 | 312 |
| area under glucose response curve | Intraperitoneal glucose tolerance test (IPGTT) | 11466 | 11410 | 9.2524909 | 0.0477476 | 0.0066496 | 0.1271584 | 9.6786741 | 0.0477437 | 0.5623652 | 0.1263608 | -0.4261833 | 0.0045072 | 0.0000000 | -0.5557156 | 0.0387755 | 0.0000000 | 0.3298651 | 0.3066156 | 0.4875969 | 0.7026479 | Metabolism | area under glucose response curve | 0.0730888 | 0.0000875 | 0.0729174 | 0.0732602 | 7.8156948 | 0.0000000 | 0.5329029 | 0.0000437 | 313 |
| fasted blood glucose concentration | Intraperitoneal glucose tolerance test (IPGTT) | 11523 | 11489 | 4.5049482 | 0.0485799 | 0.0315139 | 0.0577175 | 4.5717742 | 0.0485825 | 0.2666227 | 0.0575975 | -0.0668260 | 0.0019992 | 0.0000000 | -0.2351088 | 0.0169918 | 0.0000000 | 0.1356964 | 0.1397196 | 0.1579983 | 0.8392555 | Metabolism | fasted blood glucose concentration | -0.0292176 | 0.0000869 | -0.0293880 | -0.0290472 | -3.1336455 | 0.0017287 | 0.1593331 | 0.0000435 | 314 |
| glucose | Clinical Chemistry | 9104 | 9141 | 5.1613040 | 0.1520510 | -0.0662852 | 0.0691805 | 5.2453198 | 0.1520522 | 0.2155562 | 0.0684134 | -0.0840158 | 0.0026893 | 0.0000000 | -0.2818413 | 0.0223825 | 0.0000000 | 0.1739337 | 0.1657512 | 0.0650435 | 0.9715005 | Metabolism | glucose | 0.0481865 | 0.0001097 | 0.0479716 | 0.0484015 | 4.6016158 | 0.0000042 | 0.0651354 | 0.0000548 | 315 |
| initial response to glucose challenge | Intraperitoneal glucose tolerance test (IPGTT) | 11503 | 11460 | 5.4185019 | 0.0260089 | -0.1908838 | 0.0774606 | 5.5309672 | 0.0260193 | -0.0779889 | 0.0771464 | -0.1124652 | 0.0035109 | 0.0000000 | -0.1128950 | 0.0302074 | 0.0001865 | 0.2485309 | 0.2502005 | 0.2000295 | 0.5258545 | Metabolism | initial response to glucose challenge | -0.0066955 | 0.0000871 | -0.0068662 | -0.0065247 | -0.7173344 | 0.4731751 | 0.2027633 | 0.0000436 | 316 |
| insulin | Insulin Blood Level | 1186 | 1206 | 6.4258237 | 0.2247195 | 1.5506547 | 0.2894444 | 7.0458931 | 0.2244637 | 2.9955924 | 0.2631879 | -0.6200694 | 0.0280350 | 0.0000000 | -1.4449377 | 0.2644824 | 0.0000001 | 0.7118164 | 0.6067885 | 0.4183241 | 0.7361703 | Metabolism | insulin | 0.1596467 | 0.0008383 | 0.1580037 | 0.1612897 | 5.5139767 | 0.0000000 | 0.4456589 | 0.0004186 | 317 |
| respiratory exchange ratio | Indirect Calorimetry | 2859 | 6426 | -0.0878552 | 0.0091963 | -0.0787915 | 0.0141760 | -0.0893660 | 0.0091742 | -0.0607750 | 0.0117433 | 0.0015107 | 0.0010003 | 0.1309945 | -0.0180166 | 0.0105319 | 0.0871835 | 0.0354198 | 0.0335571 | 0.1143967 | 0.8000125 | Metabolism | respiratory exchange ratio | 0.0541179 | 0.0002529 | 0.0536222 | 0.0546136 | 3.4029323 | 0.0006695 | 0.1148997 | 0.0001077 | 318 |
| total food intake | Indirect Calorimetry | 2489 | 3941 | 1.2363388 | 0.1226354 | 0.0853943 | 0.1249996 | 1.2443269 | 0.1225525 | -0.0406682 | 0.1130341 | -0.0079881 | 0.0080256 | 0.3196238 | 0.1260625 | 0.0834596 | 0.1309834 | 0.2564772 | 0.2641866 | 0.0128038 | 0.8790484 | Metabolism | total food intake | -0.0295417 | 0.0003281 | -0.0301848 | -0.0288987 | -1.6309347 | 0.1029531 | 0.0128045 | 0.0001556 | 319 |
| total water intake | Indirect Calorimetry | 1083 | 2502 | 1.8225572 | 0.4941876 | 0.3649414 | 0.1015281 | 1.7817510 | 0.4941400 | -0.0275922 | 0.0720001 | 0.0408062 | 0.0090048 | 0.0000061 | 0.3925336 | 0.0834850 | 0.0000027 | 0.2324701 | 0.1344220 | 0.0192432 | 0.9933979 | Metabolism | total water intake | 0.5480407 | 0.0006630 | 0.5467411 | 0.5493402 | 21.2834459 | 0.0000000 | 0.0192456 | 0.0002792 | 320 |
| body length | Body Composition (DEXA lean/fat) | 8439 | 8475 | 2.2412737 | 0.0124780 | 0.1663601 | 0.0109692 | 2.2698730 | 0.0124799 | 0.1480470 | 0.0108763 | -0.0285993 | 0.0003677 | 0.0000000 | 0.0183131 | 0.0033227 | 0.0000000 | 0.0223031 | 0.0228576 | 0.3404286 | 0.9414575 | Morphology | body length | -0.0245585 | 0.0001183 | -0.0247903 | -0.0243267 | -2.2580424 | 0.0239557 | 0.3545773 | 0.0000591 | 321 |
| bone area | Body Composition (DEXA lean/fat) | 10900 | 10938 | 2.1179306 | 0.0293163 | 0.5243043 | 0.1229944 | 2.1570189 | 0.0293149 | 0.4323412 | 0.1228813 | -0.0390883 | 0.0011956 | 0.0000000 | 0.0919631 | 0.0112950 | 0.0000000 | 0.0808242 | 0.0839176 | 0.3484155 | 0.8487871 | Morphology | bone area | -0.0375594 | 0.0000916 | -0.0377389 | -0.0373798 | -3.9241823 | 0.0000873 | 0.3636392 | 0.0000458 | 322 |
| bone mineral content (excluding skull) | Body Composition (DEXA lean/fat) | 10900 | 10938 | -0.8284065 | 0.0630295 | 0.8212371 | 0.1262368 | -0.7832832 | 0.0630316 | 0.6700202 | 0.1261120 | -0.0451233 | 0.0013893 | 0.0000000 | 0.1512169 | 0.0131486 | 0.0000000 | 0.0932815 | 0.0999994 | 0.3528928 | 0.9104542 | Morphology | bone mineral content (excluding skull) | -0.0695420 | 0.0000916 | -0.0697216 | -0.0693625 | -7.2657166 | 0.0000000 | 0.3687442 | 0.0000458 | 323 |
| bone mineral density (excluding skull) | Body Composition (DEXA lean/fat) | 10901 | 10938 | -2.9601149 | 0.0693327 | 0.2549378 | 0.0324580 | -2.9543983 | 0.0693330 | 0.1914449 | 0.0324470 | -0.0057166 | 0.0007106 | 0.0000000 | 0.0634929 | 0.0066709 | 0.0000000 | 0.0442323 | 0.0522456 | 0.1165628 | 0.9734719 | Morphology | bone mineral density (excluding skull) | -0.1664996 | 0.0000916 | -0.1666791 | -0.1663200 | -17.3961915 | 0.0000000 | 0.1170951 | 0.0000458 | 324 |
| fat mass | Body Composition (DEXA lean/fat) | 11011 | 11011 | 1.2085546 | 0.1261982 | 1.9228267 | 0.1530735 | 1.5137019 | 0.1262150 | 1.6023739 | 0.1529669 | -0.3051473 | 0.0028424 | 0.0000000 | 0.3204528 | 0.0266743 | 0.0000000 | 0.1725925 | 0.2152742 | 0.4487833 | 0.9311609 | Morphology | fat mass | -0.2209793 | 0.0000908 | -0.2211574 | -0.2208013 | -23.1849345 | 0.0000000 | 0.4831757 | 0.0000454 | 325 |
| fat/body weight | Body Composition (DEXA lean/fat) | 11000 | 11008 | -1.9415379 | 0.1264290 | 0.9267682 | 0.1565237 | -1.8427698 | 0.1264454 | 0.6048693 | 0.1564046 | -0.0987681 | 0.0028439 | 0.0000000 | 0.3218989 | 0.0266914 | 0.0000000 | 0.1726149 | 0.2154048 | 0.1955597 | 0.9165604 | Morphology | fat mass | -0.2214560 | 0.0000909 | -0.2216342 | -0.2212778 | -23.2275543 | 0.0000000 | 0.1981115 | 0.0000454 | 326 |
| lean mass | Body Composition (DEXA lean/fat) | 11012 | 11012 | 2.8126612 | 0.0270981 | 0.7805976 | 0.0421663 | 3.0075607 | 0.0271029 | 0.7914631 | 0.0421922 | -0.1948995 | 0.0007378 | 0.0000000 | -0.0108655 | 0.0068882 | 0.1147183 | 0.0422704 | 0.0570401 | 0.7236142 | 0.9602351 | Morphology | lean mass | -0.2996680 | 0.0000908 | -0.2998460 | -0.2994900 | -31.4423014 | 0.0000000 | 0.9151904 | 0.0000454 | 327 |
| lean/body weight | Body Composition (DEXA lean/fat) | 11001 | 11009 | -0.3415181 | 0.0269983 | -0.1849234 | 0.0529737 | -0.3531169 | 0.0270024 | -0.1753142 | 0.0529567 | 0.0115988 | 0.0007367 | 0.0000000 | -0.0096093 | 0.0068790 | 0.1624604 | 0.0421416 | 0.0570452 | 0.1628020 | 0.9141149 | Morphology | lean mass | -0.3028074 | 0.0000909 | -0.3029856 | -0.3026293 | -31.7615950 | 0.0000000 | 0.1642636 | 0.0000454 | 328 |
| left kidney | Organ Weight | 1066 | 1118 | -1.9571335 | 0.0043280 | 0.6740011 | 0.0373886 | -1.6927585 | 0.0040261 | 0.7366847 | 0.0343801 | -0.2643750 | 0.0042352 | 0.0000000 | -0.0626836 | 0.0496439 | 0.2068567 | 0.0992489 | 0.0869714 | 0.8225738 | 0.8478376 | Morphology | kidney weight | 0.1320729 | 0.0009188 | 0.1302721 | 0.1338737 | 4.3571629 | 0.0000138 | 1.1647252 | 0.0004585 | 329 |
| number of caudal vertebrae | X-ray | 2789 | 2739 | 3.2352323 | 0.0400332 | 0.0108036 | 0.0072612 | 3.2350310 | 0.0400327 | 0.0127032 | 0.0072410 | 0.0002012 | 0.0005199 | 0.6986983 | -0.0018996 | 0.0054825 | 0.7289952 | 0.0192721 | 0.0177723 | 0.0123539 | 0.9803384 | Morphology | number of caudal vertebrae | 0.0810170 | 0.0003622 | 0.0803070 | 0.0817269 | 4.2568797 | 0.0000211 | 0.0123546 | 0.0001810 | 330 |
| number of center entries | Open Field | 6567 | 6540 | 4.5079198 | 0.2385742 | -0.0883596 | 0.1227921 | 4.4857706 | 0.2385843 | 0.0337060 | 0.1212838 | 0.0221492 | 0.0068910 | 0.0013116 | -0.1220656 | 0.0719422 | 0.0897767 | 0.3461157 | 0.3326489 | 0.0146955 | 0.9155008 | Morphology | number of center entries | 0.0396852 | 0.0001527 | 0.0393860 | 0.0399844 | 3.2119228 | 0.0013217 | 0.0146965 | 0.0000763 | 331 |
| number of cervical vertebrae | X-ray | 4714 | 4664 | 1.9456484 | 0.0001966 | -0.0001302 | 0.0007736 | 1.9456530 | 0.0001984 | -0.0001620 | 0.0007783 | -0.0000046 | 0.0000925 | 0.9604395 | 0.0000317 | 0.0009280 | 0.9727368 | 0.0041156 | 0.0044365 | 0.0020896 | 0.7718660 | Morphology | number of cervical vertebrae | -0.0750947 | 0.0002134 | -0.0755130 | -0.0746765 | -5.1404884 | 0.0000003 | 0.0020896 | 0.0001067 | 332 |
| number of digits | X-ray | 5769 | 5715 | 2.9958120 | 0.0000601 | 0.0000760 | 0.0003467 | 2.9958074 | 0.0000585 | 0.0001352 | 0.0003078 | 0.0000046 | 0.0000355 | 0.8961433 | -0.0000592 | 0.0003688 | 0.8724862 | 0.0020486 | 0.0017195 | 0.0076193 | 0.0883626 | Morphology | number of digits | 0.1751167 | 0.0001743 | 0.1747752 | 0.1754583 | 13.2660293 | 0.0000000 | 0.0076195 | 0.0000871 | 333 |
| number of lumbar vertebrae | X-ray | 4714 | 4664 | 1.7916283 | 0.0001513 | -0.0003992 | 0.0010497 | 1.7909572 | 0.0002275 | -0.0018162 | 0.0018873 | 0.0006711 | 0.0002226 | 0.0025786 | 0.0014171 | 0.0021396 | 0.5077901 | 0.0070205 | 0.0135034 | 0.0323075 | 0.0454164 | Morphology | number of lumbar vertebrae | -0.6541153 | 0.0002134 | -0.6545336 | -0.6536970 | -44.7764096 | 0.0000000 | 0.0323188 | 0.0001067 | 334 |
| number of pelvic vertebrae | X-ray | 4716 | 4664 | 1.3865015 | 0.0001900 | 0.0000485 | 0.0013639 | 1.3870294 | 0.0002486 | 0.0028625 | 0.0019857 | -0.0005279 | 0.0002473 | 0.0328476 | -0.0028140 | 0.0023876 | 0.2385936 | 0.0091843 | 0.0142089 | 0.0276818 | 0.0386085 | Morphology | number of pelvic vertebrae | -0.4363735 | 0.0002134 | -0.4367917 | -0.4359554 | -29.8744010 | 0.0000000 | 0.0276889 | 0.0001066 | 335 |
| number of rears - total | Open Field | 5470 | 5426 | 4.7567552 | 0.1095900 | 0.3878560 | 0.1391049 | 4.8804103 | 0.1096147 | 0.6496257 | 0.1315288 | -0.1236551 | 0.0094971 | 0.0000000 | -0.2617697 | 0.0908771 | 0.0039797 | 0.4857208 | 0.4486118 | 0.1356474 | 0.6650416 | Morphology | number of rears - total | 0.0794752 | 0.0001837 | 0.0791152 | 0.0798351 | 5.8644430 | 0.0000000 | 0.1364887 | 0.0000918 | 336 |
| number of ribs right | X-ray | 5854 | 5791 | 2.5649042 | 0.0000714 | -0.0004702 | 0.0004871 | 2.5648535 | 0.0000753 | -0.0003631 | 0.0005276 | 0.0000507 | 0.0000585 | 0.3863560 | -0.0001071 | 0.0006021 | 0.8588647 | 0.0028121 | 0.0033558 | 0.0145383 | 0.2465621 | Morphology | number of ribs | -0.1767628 | 0.0001718 | -0.1770996 | -0.1764260 | -13.4842662 | 0.0000000 | 0.0145393 | 0.0000859 | 337 |
| number of signals | Electrocardiogram (ECG) | 6006 | 5936 | 3.1485022 | 0.2362184 | -0.0383841 | 0.0763915 | 3.1562963 | 0.2362239 | 0.0130587 | 0.0750447 | -0.0077941 | 0.0059341 | 0.1890652 | -0.0514427 | 0.0555846 | 0.3547351 | 0.3001648 | 0.3028353 | 0.0073742 | 0.9136437 | Morphology | number of signals | -0.0088585 | 0.0001676 | -0.0091869 | -0.0085300 | -0.6843286 | 0.4937810 | 0.0073744 | 0.0000838 | 338 |
| number of thoracic vertebrae | X-ray | 4714 | 4664 | 2.5649651 | 0.0000157 | 0.0000320 | 0.0001570 | 2.5649652 | 0.0000159 | -0.0000639 | 0.0001510 | -0.0000002 | 0.0000224 | 0.9938539 | 0.0000959 | 0.0002178 | 0.6597688 | 0.0010793 | 0.0010851 | 0.0048641 | 0.0049778 | Morphology | number of thoracic vertebrae | -0.0053151 | 0.0002134 | -0.0057334 | -0.0048968 | -0.3638353 | 0.7159892 | 0.0048642 | 0.0001067 | 339 |
| right kidney | Organ Weight | 1067 | 1120 | -1.9049610 | 0.0041098 | 0.6891891 | 0.0377253 | -1.6439090 | 0.0038316 | 0.7658913 | 0.0353447 | -0.2610520 | 0.0042982 | 0.0000000 | -0.0767021 | 0.0506281 | 0.1299263 | 0.1003178 | 0.0903670 | 0.8199338 | 0.8391281 | Morphology | kidney weight | 0.1044864 | 0.0009176 | 0.1026880 | 0.1062848 | 3.4494056 | 0.0005725 | 1.1566154 | 0.0004579 | 340 |
| spleen weight | Immunophenotyping | 3267 | 3274 | -1.8465148 | 0.6351810 | 0.7278822 | 0.0738659 | -1.9330913 | 0.6351908 | 0.8994422 | 0.0824774 | 0.0865766 | 0.0048530 | 0.0000000 | -0.1715600 | 0.0534932 | 0.0013485 | 0.1257955 | 0.2370918 | 0.0438323 | 0.9953358 | Morphology | spleen weight | -0.6337896 | 0.0003060 | -0.6343895 | -0.6331898 | -36.2286951 | 0.0000000 | 0.0438604 | 0.0001530 | 341 |
| tibia length | X-ray | 5527 | 5485 | 2.8915021 | 0.0062539 | 0.1199794 | 0.0084998 | 2.9005087 | 0.0062551 | 0.0727301 | 0.0083672 | -0.0090066 | 0.0003548 | 0.0000000 | 0.0472492 | 0.0038205 | 0.0000000 | 0.0167151 | 0.0169092 | 0.2563153 | 0.9033320 | Morphology | tibia length | -0.0115483 | 0.0001817 | -0.0119045 | -0.0111921 | -0.8566720 | 0.3916448 | 0.2621606 | 0.0000908 | 342 |
| alanine aminotransferase | Clinical Chemistry | 8396 | 8423 | 3.4958300 | 0.0793574 | -0.4158702 | 0.1153201 | 3.6197826 | 0.0793852 | -0.1667243 | 0.1135564 | -0.1239525 | 0.0059241 | 0.0000000 | -0.2491459 | 0.0523192 | 0.0000019 | 0.3536738 | 0.3704474 | 0.1354871 | 0.7239137 | Physiology | alanine aminotransferase | -0.0463364 | 0.0001190 | -0.0465696 | -0.0461033 | -4.2484397 | 0.0000216 | 0.1363254 | 0.0000595 | 343 |
| albumin | Clinical Chemistry | 8422 | 8460 | 3.4224253 | 0.0268218 | 0.0347068 | 0.0375982 | 3.3598140 | 0.0268261 | 0.0221379 | 0.0376410 | 0.0626113 | 0.0008412 | 0.0000000 | 0.0125689 | 0.0073213 | 0.0860445 | 0.0431837 | 0.0575406 | 0.2373167 | 0.9238670 | Physiology | albumin | -0.2870275 | 0.0001185 | -0.2872597 | -0.2867952 | -26.3658704 | 0.0000000 | 0.2419287 | 0.0000592 | 344 |
| albumin to creatinine ratio | Urinalysis | 363 | 362 | 2.5349933 | 0.0567353 | -0.9499331 | 0.3614808 | 2.9409462 | 0.0552237 | -0.1211961 | 0.3365862 | -0.4059530 | 0.0261393 | 0.0000000 | -0.8287369 | 0.2833810 | 0.0035667 | 0.3940953 | 0.3020907 | 0.4537135 | 0.6605510 | Physiology | albumin to creatinine ratio | 0.2658614 | 0.0027816 | 0.2604095 | 0.2713134 | 5.0408570 | 0.0000006 | 0.4893665 | 0.0013850 | 345 |
| alkaline phosphatase | Clinical Chemistry | 8343 | 8388 | 5.0047139 | 0.1349745 | -0.4412197 | 0.0857331 | 4.5636446 | 0.1349774 | -0.3506908 | 0.0855144 | 0.4410693 | 0.0019420 | 0.0000000 | -0.0905290 | 0.0170592 | 0.0000001 | 0.1110852 | 0.1224454 | 0.3719792 | 0.9811964 | Physiology | alkaline phosphatase | -0.0973674 | 0.0001196 | -0.0976018 | -0.0971330 | -8.9038942 | 0.0000000 | 0.3907181 | 0.0000598 | 346 |
| alpha-amylase | Clinical Chemistry | 5517 | 5586 | 6.3655692 | 0.0709663 | 0.2939984 | 0.0509810 | 6.5825975 | 0.0709663 | 0.4279371 | 0.0510520 | -0.2170284 | 0.0021362 | 0.0000000 | -0.1339386 | 0.0226459 | 0.0000000 | 0.1039605 | 0.1056308 | 0.4694433 | 0.9020493 | Physiology | alpha-amylase | -0.0159376 | 0.0001802 | -0.0162908 | -0.0155843 | -1.1871390 | 0.2351982 | 0.5093560 | 0.0000901 | 347 |
| aspartate aminotransferase | Clinical Chemistry | 8332 | 8368 | 4.3122509 | 0.1152550 | -0.8017137 | 0.1690837 | 4.2238842 | 0.1152691 | -0.4101947 | 0.1678576 | 0.0883666 | 0.0054019 | 0.0000000 | -0.3915190 | 0.0477379 | 0.0000000 | 0.3245266 | 0.3273582 | 0.1345821 | 0.8485962 | Physiology | aspartate aminotransferase | -0.0086871 | 0.0001198 | -0.0089219 | -0.0084523 | -0.7936657 | 0.4274014 | 0.1354035 | 0.0000599 | 348 |
| body temp | Echo | 1057 | 491 | 3.6074441 | 0.0014635 | 0.0097565 | 0.0091966 | 3.6072148 | 0.0015769 | -0.0019405 | 0.0103538 | 0.0002293 | 0.0012727 | 0.8570567 | 0.0116970 | 0.0133968 | 0.3827678 | 0.0212316 | 0.0163752 | 0.0303531 | 0.4806816 | Physiology | body temp | 0.2591721 | 0.0014990 | 0.2562341 | 0.2621100 | 6.6940852 | 0.0000000 | 0.0303624 | 0.0006472 | 349 |
| calcium | Clinical Chemistry | 8366 | 8425 | 2.2039137 | 0.0183643 | 0.0616814 | 0.0315951 | 2.2066718 | 0.0183650 | 0.0604948 | 0.0315525 | -0.0027581 | 0.0004430 | 0.0000000 | 0.0011866 | 0.0038848 | 0.7600355 | 0.0260472 | 0.0265003 | 0.0659663 | 0.9687371 | Physiology | calcium | -0.0172464 | 0.0001192 | -0.0174799 | -0.0170128 | -1.5799419 | 0.1141390 | 0.0660622 | 0.0000596 | 350 |
| chloride | Clinical Chemistry | 6176 | 6149 | 4.6992779 | 0.0113535 | 0.0194164 | 0.0248603 | 4.6855784 | 0.0113539 | 0.0020013 | 0.0248613 | 0.0136995 | 0.0002712 | 0.0000000 | 0.0174152 | 0.0024671 | 0.0000000 | 0.0143069 | 0.0134362 | 0.1281648 | 0.9676712 | Physiology | chloride | 0.0627923 | 0.0001624 | 0.0624741 | 0.0631105 | 4.9280834 | 0.0000008 | 0.1288735 | 0.0000812 | 351 |
| creatine kinase | Clinical Chemistry | 4339 | 4390 | 5.3733636 | 0.2585420 | -1.0607231 | 0.2797632 | 5.1158627 | 0.2585278 | -1.1459206 | 0.2820566 | 0.2575009 | 0.0161357 | 0.0000000 | 0.0851975 | 0.1711916 | 0.6187282 | 0.7033179 | 0.6883311 | 0.1634626 | 0.7262593 | Physiology | creatine kinase | 0.0215404 | 0.0002293 | 0.0210910 | 0.0219898 | 1.4225385 | 0.1549057 | 0.1649423 | 0.0001146 | 352 |
| creatinine | Clinical Chemistry | 7564 | 7667 | -1.2108486 | 0.4918701 | -0.1478675 | 0.0917161 | -1.3420870 | 0.4918710 | -0.3363327 | 0.0910909 | 0.1312384 | 0.0029866 | 0.0000000 | 0.1884652 | 0.0260836 | 0.0000000 | 0.1689110 | 0.1684454 | 0.0352974 | 0.9965951 | Physiology | creatinine | 0.0027614 | 0.0001314 | 0.0025039 | 0.0030188 | 0.2409228 | 0.8096182 | 0.0353121 | 0.0000657 | 353 |
| free fatty acids | Clinical Chemistry | 3135 | 3249 | -0.1908926 | 0.3355867 | 0.2051765 | 0.0590988 | -0.1821257 | 0.3355800 | 0.1852278 | 0.0564134 | -0.0087669 | 0.0067241 | 0.1923558 | 0.0199487 | 0.0486543 | 0.6818147 | 0.2735448 | 0.2555267 | 0.0374689 | 0.9357761 | Physiology | free fatty acids | 0.0681442 | 0.0003137 | 0.0675294 | 0.0687590 | 3.8475730 | 0.0001205 | 0.0374864 | 0.0001567 | 354 |
| fructosamine | Clinical Chemistry | 4390 | 4468 | 5.4141435 | 0.0786843 | 0.0755157 | 0.0088269 | 5.3837355 | 0.0786844 | 0.1163464 | 0.0098485 | 0.0304080 | 0.0012088 | 0.0000000 | -0.0408307 | 0.0125975 | 0.0011953 | 0.0509774 | 0.0541242 | 0.1112335 | 0.9447652 | Physiology | fructosamine | -0.0598967 | 0.0002260 | -0.0603396 | -0.0594538 | -3.9846650 | 0.0000681 | 0.1116957 | 0.0001129 | 355 |
| glycerol | Clinical Chemistry | 1977 | 2014 | -2.1264333 | 0.0455114 | 0.4488870 | 0.0947463 | -2.0445457 | 0.0455545 | 0.8888700 | 0.1011505 | -0.0818876 | 0.0066489 | 0.0000000 | -0.4399830 | 0.0697895 | 0.0000000 | 0.1961864 | 0.2172242 | 0.3018914 | 0.6003661 | Physiology | glycerol | -0.1018597 | 0.0005019 | -0.1028435 | -0.1008760 | -4.5465603 | 0.0000056 | 0.3115994 | 0.0002508 | 356 |
| hdl cholesterol | Plasma Chemistry | 483 | 470 | 4.0516902 | 0.0918187 | 0.4742748 | 0.1526706 | 4.3767068 | 0.0917515 | 0.3872788 | 0.1403269 | -0.3250166 | 0.0081496 | 0.0000000 | 0.0869960 | 0.0946188 | 0.3581217 | 0.1374655 | 0.1117679 | 0.5992480 | 0.8939946 | Physiology | hdl-cholesterol | 0.2069192 | 0.0021123 | 0.2027791 | 0.2110593 | 4.5021484 | 0.0000076 | 0.6919730 | 0.0010526 | 357 |
| hdl-cholesterol | Clinical Chemistry | 8305 | 8353 | 3.9859773 | 0.0332479 | 0.2921303 | 0.0737425 | 4.2288871 | 0.0332528 | 0.2908496 | 0.0730626 | -0.2429099 | 0.0022669 | 0.0000000 | 0.0012807 | 0.0200340 | 0.9490294 | 0.1412334 | 0.1340372 | 0.5265656 | 0.8172148 | Physiology | hdl-cholesterol | 0.0522965 | 0.0001201 | 0.0520611 | 0.0525319 | 4.7718737 | 0.0000018 | 0.5853813 | 0.0000600 | 358 |
| iron | Clinical Chemistry | 6746 | 6815 | -2.0503364 | 0.0580229 | 0.2400050 | 0.0277708 | -2.1939673 | 0.0580160 | 0.1141657 | 0.0258954 | 0.1436309 | 0.0024945 | 0.0000000 | 0.1258393 | 0.0234878 | 0.0000001 | 0.1490667 | 0.1242727 | 0.3226502 | 0.8025156 | Physiology | iron | 0.1819162 | 0.0001476 | 0.1816271 | 0.1822054 | 14.9761651 | 0.0000000 | 0.3346025 | 0.0000738 | 359 |
| lactate dehydrogenase | Clinical Chemistry | 540 | 542 | 5.5795641 | 0.1249101 | -0.5289496 | 0.3402740 | 5.6750063 | 0.1250768 | -0.4852539 | 0.3481930 | -0.0954423 | 0.0244280 | 0.0000999 | -0.0436957 | 0.3056995 | 0.8863699 | 0.3293357 | 0.3537948 | 0.1289608 | 0.6927776 | Physiology | lactate dehydrogenase | -0.0716361 | 0.0018587 | -0.0752791 | -0.0679930 | -1.6615831 | 0.0968866 | 0.1296830 | 0.0009268 | 360 |
| ldl-cholesterol | Clinical Chemistry | 2576 | 2619 | 2.0933496 | 0.2218465 | 0.4532298 | 0.1715225 | 2.0894050 | 0.2218558 | 1.4619696 | 0.1721564 | 0.0039447 | 0.0055376 | 0.4762913 | -1.0087397 | 0.0584183 | 0.0000000 | 0.1776930 | 0.1991605 | 0.1758604 | 0.9448329 | Physiology | ldl-cholesterol | -0.1140504 | 0.0003855 | -0.1148059 | -0.1132949 | -5.8090995 | 0.0000000 | 0.1777077 | 0.0001926 | 361 |
| lipase | Clinical Chemistry | 1182 | 1199 | 4.0630722 | 0.0150335 | -0.0115728 | 0.1686217 | 4.0158245 | 0.0146907 | -0.2269597 | 0.1570808 | 0.0472476 | 0.0095930 | 0.0000009 | 0.2153868 | 0.1188474 | 0.0700806 | 0.2402708 | 0.2152829 | 0.1119598 | 0.3957916 | Physiology | lipase | 0.1098196 | 0.0008421 | 0.1081690 | 0.1114702 | 3.7842993 | 0.0001579 | 0.1124312 | 0.0004205 | 362 |
| magnesium | Clinical Chemistry | 2380 | 2372 | 1.9540612 | 0.5410852 | 0.0398971 | 0.0254176 | 1.8658023 | 0.5410862 | -0.1418447 | 0.0255750 | 0.0882589 | 0.0018609 | 0.0000000 | 0.1817419 | 0.0146564 | 0.0000000 | 0.0638186 | 0.0618207 | 0.0425724 | 0.9983206 | Physiology | magnesium | 0.0318070 | 0.0004214 | 0.0309810 | 0.0326329 | 1.5494243 | 0.1213464 | 0.0425981 | 0.0002106 | 363 |
| microalbumin (calculated) | Urinalysis | 358 | 356 | -0.2234848 | 0.1121445 | -1.5126761 | 0.5371663 | 0.2003426 | 0.1110445 | -0.6221435 | 0.5134080 | -0.4238274 | 0.0298527 | 0.0000000 | -0.8905325 | 0.3244866 | 0.0062258 | 0.4522271 | 0.3352127 | 0.4336523 | 0.6782556 | Physiology | microalbumin (calculated) | 0.2994113 | 0.0028249 | 0.2938747 | 0.3049480 | 5.6333684 | 0.0000000 | 0.4643862 | 0.0014065 | 364 |
| phosphorus | Clinical Chemistry | 8332 | 8421 | 1.8883138 | 0.0453832 | -0.0153507 | 0.0524466 | 1.8455942 | 0.0453871 | 0.0677412 | 0.0512168 | 0.0427196 | 0.0026315 | 0.0000000 | -0.0830919 | 0.0233601 | 0.0003763 | 0.1626681 | 0.1553781 | 0.0819884 | 0.8079207 | Physiology | phosphorus | 0.0458513 | 0.0001194 | 0.0456172 | 0.0460854 | 4.1956488 | 0.0000273 | 0.0821729 | 0.0000597 | 365 |
| potassium | Clinical Chemistry | 6153 | 6110 | 1.5406998 | 0.0715641 | -0.2157248 | 0.0563259 | 1.6148865 | 0.0715662 | -0.0629098 | 0.0560910 | -0.0741867 | 0.0017524 | 0.0000000 | -0.1528150 | 0.0159732 | 0.0000000 | 0.0958113 | 0.0852794 | 0.1724382 | 0.9243719 | Physiology | potassium | 0.1164478 | 0.0001632 | 0.1161280 | 0.1167677 | 9.1160332 | 0.0000000 | 0.1741785 | 0.0000816 | 366 |
| sodium | Clinical Chemistry | 6174 | 6141 | 4.9859499 | 0.0079379 | 0.0242208 | 0.0221218 | 4.9955314 | 0.0079383 | 0.0166908 | 0.0221240 | -0.0095815 | 0.0002241 | 0.0000000 | 0.0075300 | 0.0020379 | 0.0002209 | 0.0118586 | 0.0110433 | 0.1085883 | 0.9727450 | Physiology | sodium | 0.0712307 | 0.0001625 | 0.0709122 | 0.0715491 | 5.5880689 | 0.0000000 | 0.1090181 | 0.0000812 | 367 |
| thyroxine | Clinical Chemistry | 1451 | 1465 | 1.4896248 | 0.0138528 | -0.0089023 | 0.1204208 | 1.3945864 | 0.0136463 | 0.1647836 | 0.1210199 | 0.0950384 | 0.0058873 | 0.0000000 | -0.1736858 | 0.0597145 | 0.0036606 | 0.1674618 | 0.1409476 | 0.2583731 | 0.5721188 | Physiology | thyroxine | 0.1723703 | 0.0006873 | 0.1710232 | 0.1737173 | 6.5748948 | 0.0000000 | 0.2643643 | 0.0003433 | 368 |
| total bilirubin | Clinical Chemistry | 8250 | 8216 | -2.7899507 | 0.3036825 | 0.2856520 | 0.3884217 | -2.8461449 | 0.3036948 | 0.4605417 | 0.3877488 | 0.0561946 | 0.0069990 | 0.0000000 | -0.1748903 | 0.0615284 | 0.0044832 | 0.4155367 | 0.4247432 | 0.0405959 | 0.9452052 | Physiology | total bilirubin | -0.0219141 | 0.0001215 | -0.0221522 | -0.0216759 | -1.9880245 | 0.0468255 | 0.0406182 | 0.0000607 | 369 |
| total cholesterol | Clinical Chemistry | 8895 | 8888 | 4.4559610 | 0.0294454 | 0.2938856 | 0.0538597 | 4.6437123 | 0.0294519 | 0.4031854 | 0.0531709 | -0.1877513 | 0.0020482 | 0.0000000 | -0.1092998 | 0.0173679 | 0.0000000 | 0.1303677 | 0.1266791 | 0.4648359 | 0.8142926 | Physiology | total cholesterol | 0.0287020 | 0.0001125 | 0.0284815 | 0.0289225 | 2.7059873 | 0.0068167 | 0.5034625 | 0.0000562 | 370 |
| total protein | Clinical Chemistry | 8348 | 8441 | 3.8849706 | 0.0139439 | 0.0611388 | 0.0268146 | 3.8889390 | 0.0139448 | 0.0644660 | 0.0266482 | -0.0039684 | 0.0006659 | 0.0000000 | -0.0033271 | 0.0059094 | 0.5734252 | 0.0412034 | 0.0384542 | 0.0935436 | 0.8755738 | Physiology | total protein | 0.0690546 | 0.0001192 | 0.0688210 | 0.0692882 | 6.3256577 | 0.0000000 | 0.0938179 | 0.0000596 | 371 |
| triglycerides | Clinical Chemistry | 8654 | 8690 | 4.2826994 | 0.0914312 | 0.4877920 | 0.1240281 | 4.6155055 | 0.0914368 | 0.9631632 | 0.1220962 | -0.3328061 | 0.0047200 | 0.0000000 | -0.4753712 | 0.0403605 | 0.0000000 | 0.3052526 | 0.2808901 | 0.3518302 | 0.8426002 | Physiology | triglycerides | 0.0831763 | 0.0001154 | 0.0829502 | 0.0834024 | 7.7443204 | 0.0000000 | 0.3675310 | 0.0000577 | 372 |
| uibc (unsaturated iron binding capacity) | Clinical Chemistry | 1207 | 1236 | 3.4482618 | 0.0238103 | 0.1195660 | 0.1096770 | 3.5497727 | 0.0234192 | 0.4122182 | 0.0908647 | -0.1015110 | 0.0067085 | 0.0000000 | -0.2926522 | 0.0849728 | 0.0005838 | 0.1926020 | 0.1220096 | 0.3087742 | 0.5205858 | Physiology | uibc (unsaturated iron binding capacity) | 0.4565358 | 0.0008208 | 0.4549271 | 0.4581445 | 15.9351759 | 0.0000000 | 0.3191899 | 0.0004098 | 373 |
| urea (blood urea nitrogen - bun) | Clinical Chemistry | 8307 | 8434 | 3.1788867 | 0.0399099 | -0.0152174 | 0.0790974 | 3.2007494 | 0.0399025 | 0.0009257 | 0.0779456 | -0.0218627 | 0.0023504 | 0.0000000 | -0.0161431 | 0.0212422 | 0.4472930 | 0.1600475 | 0.1238227 | 0.0472787 | 0.7917277 | Physiology | urea (blood urea nitrogen - bun) | 0.2566207 | 0.0001195 | 0.2563865 | 0.2568550 | 23.4734565 | 0.0000000 | 0.0473140 | 0.0000597 | 374 |
| uric acid | Clinical Chemistry | 359 | 357 | 2.7214710 | 0.2264073 | -0.1671090 | 0.4596686 | 2.9020553 | 0.2266386 | 0.0691049 | 0.4410877 | -0.1805843 | 0.0427804 | 0.0000278 | -0.2362140 | 0.6185586 | 0.7026790 | 0.4874136 | 0.5283643 | 0.1417864 | 0.6057446 | Physiology | uric acid | -0.0806808 | 0.0028169 | -0.0862019 | -0.0751597 | -1.5201367 | 0.1289193 | 0.1427481 | 0.0014025 | 375 |
- parameter_name: the name of phenotypic traits
- f_n: the number of females for a particular trait
- m_n: the number of males for a particular trait
- f_intercept: the intercept (phenotypic mean) for females
- f_intercept_se: standard error for the intercept (phenotypic mean) for females
- f_slope: the slope for females
- f_slope_se: standard error for the slope for females
- m_intercept: the intercept (phenotypic mean) for males
- m_intercept_se: standard error for the intercept (phenotypic mean) for males
- m_slope: the slope for males
- m_slope_se: standard error for the slope for males
- fm_diff_int: difference in intercepts between females and males
- fm_diff_int_se: standard error for the difference in intercepts between females and males
- fm_diff_int_p: p value associated with fm_diff_int
- fm_diff_slope: difference in slopes between females and males
- fm_diff_slope_se: standard error for for the difference in intercepts between females and males
- fm_diff_slope_p: p value associated with fm_diff_slope
- batch_sd: the square-root of the variance component for “batch” (see the text)
- f_sd: female residual standard deviation
- m_sd: female residual standard deviation
- r_m: marginal R squared (variance accounted for by fixed effects)
- r_c: conditional R squared (variance accounted for by fixed and random effects)
- Category: 9 function categories (see the text)
- parameter_group: Grouping for non-independent traits
- lnVR: log ratio between f_sd and m_sd
- VlnVR: the sampling variance for lnVR
- low_lnVR: lower confidence limit for lnVR
- high_lnVR: upper confidence limit for lnVR
- t_val_sd: t values associated with lnVR and VlnVR
- p_val_sd: p values associated with lnVR and VlnVR
- Zr: transformed value of sqrt(r_c)
- VZr: sampling variance for Zr
- obs: unique observation level ID
Data analysis
Preparation for categorizing into scenarios
Here, we merge p values for non-independent (closely related traits)
using the custom functions for merging p values via the
poolr package.
# here we need to collapse p values which are related split data into 2 ones
# with replications within parameter_group
dat %>%
group_by(parameter_group) %>%
mutate(count = n()) -> dat
#
dat1 <- dat[which(dat$count == 1), ]
# dim(dat1)
# taking out indepedent traits
dat2 <- dat[-which(dat$count == 1), ]
# nesting data into a lot of data sets and apply p_mod function
n_dat2 <- dat2 %>%
group_by(parameter_group) %>%
nest()
# function to get merged p value for intercepts
p_mod_int <- function(data) {
len <- dim(data)[1]
Rmat <- matrix(0.8, nrow = len, ncol = len)
diag(Rmat) <- 1
p_mod <- fisher(data$fm_diff_int_p, adjust = "liji", R = Rmat)
p <- p_mod$p
return(p)
}
# function to get merged p value for slopes
p_mod_slp <- function(data) {
len <- dim(data)[1]
Rmat <- matrix(0.8, nrow = len, ncol = len)
diag(Rmat) <- 1
p_mod <- fisher(data$fm_diff_slope_p, adjust = "liji", R = Rmat)
p <- p_mod$p
return(p)
}
# function to get merged p value for SD
p_mod_sd <- function(data) {
len <- dim(data)[1]
Rmat <- matrix(0.8, nrow = len, ncol = len)
diag(Rmat) <- 1
p_mod <- fisher(data$p_val_sd, adjust = "liji", R = Rmat)
p <- p_mod$p
return(p)
}
# merged dat2
m_dat2 <- n_dat2 %>%
mutate(merged_p_sd = map_dbl(data, p_mod_sd), merged_p_int = map_dbl(data, p_mod_int),
merged_p_slp = map_dbl(data, p_mod_slp))The number of cases Scenario A
# full dataset
dat_slopes <- dat %>%
filter(fm_diff_slope_p <= 0.05 & fm_diff_int_p > 0.05)
# 16 out of 375 traits sig slope diff - scenario A
nrow(dat_slopes)## [1] 16
# reduced dataset
dat_slopes1 <- dat1 %>%
filter(fm_diff_slope_p <= 0.05 & fm_diff_int_p > 0.05)
dat_slopes2 <- m_dat2 %>%
filter(merged_p_slp <= 0.05 & merged_p_int > 0.05)
# 11 out of 226 traits sig slope diff - scenario A
nrow(dat_slopes1) + nrow(dat_slopes2)## [1] 11
The number of Scenario B
# full dataset
dat_int <- dat %>%
filter(fm_diff_int_p <= 0.05 & fm_diff_slope_p > 0.05)
# 165 out of 375 traits sig intercept diff same slope - scenario B
nrow(dat_int)## [1] 165
# reduced dataset
dat_int1 <- dat1 %>%
filter(fm_diff_int_p <= 0.05 & fm_diff_slope_p > 0.05)
dat_int2 <- m_dat2 %>%
filter(merged_p_int <= 0.05 & merged_p_slp > 0.05)
# 93 out of 226 traits sig intercept diff same slope - scenario B
nrow(dat_int1) + nrow(dat_int2)## [1] 93
The number of Scenario C
# full dataset
dat_intSlopes <- dat %>%
filter(fm_diff_int_p <= 0.05 & fm_diff_slope_p <= 0.05)
# 81 out of 375 sig intercept and slope diff - scenario C
nrow(dat_intSlopes)## [1] 81
# reduced dataset
dat_intSlopes1 <- dat1 %>%
filter(fm_diff_int_p <= 0.05 & fm_diff_slope_p <= 0.05)
dat_intSlopes2 <- m_dat2 %>%
filter(merged_p_int <= 0.05 & merged_p_slp <= 0.05)
# 67 out of 226 sig intercept and slope diff - scenario C
nrow(dat_intSlopes1) + nrow(dat_intSlopes2)## [1] 67
Not in these scenarios
# full dataset
dat_intslopesNS <- dat %>%
filter(fm_diff_slope_p > 0.05 & fm_diff_int_p > 0.05)
# 113 out of 375 - no sig difference between intercept and slope - scenario D
nrow(dat_intslopesNS)## [1] 113
# reduced dataset
dat_intslopesNS1 <- dat1 %>%
filter(fm_diff_slope_p > 0.05 & fm_diff_int_p > 0.05)
dat_intslopesNS2 <- m_dat2 %>%
filter(merged_p_int > 0.05 & merged_p_slp > 0.05)
# 55 out of 226 no sig difference between intercept and slope - scenario D
nrow(dat_intslopesNS1) + nrow(dat_intslopesNS2)## [1] 55
Sex difference in residaul SD
# full dataset 261 out of 375 signficant differences in residual SDs
length(which(dat$p_val_sd <= 0.05))## [1] 261
# hist(log(dat$p_val_sd)) # p = 0.05 ~ - 3
# 161 of out of 226 signficant differences in residual SDs
length(which(m_dat2$merged_p_sd <= 0.05)) + length(which(dat1$p_val_sd <= 0.05))## [1] 161
Creating Figure 2
# set colour for males and females
colours <- c("#D55E00", "#009E73") # c('#882255','#E69F00')
colours2 <- c("#D55E00", "#7D26CD", "#009E73")
# sex bias in slope parameter under scenario A
dat_p1 <- dat_slopes %>%
group_by_at(vars(Category)) %>%
summarise(malebias = sum(m_slope > f_slope), femalebias = sum(f_slope > m_slope),
total = malebias + femalebias, malepercent = malebias * 100/total, femalepercent = femalebias *
100/total)
dat_p1 <- gather(as.data.frame(dat_p1), key = sex, value = percent, malepercent:femalepercent,
factor_key = TRUE)
dat_p1$samplesize <- with(dat_p1, ifelse(sex == "malepercent", malebias, femalebias))
# Adding All
dat_p1 %>%
group_by(sex) %>%
summarise(malebias = sum(malebias), femalebias = sum(femalebias), total = sum(total),
) -> part
part %>%
mutate(Category = "All", sex = c("malepercent", "femalepercent"), percent = c(100 *
(malebias[1]/total[1]), 100 * (femalebias[1]/total[1])), samplesize = c(malebias[1],
femalebias[1])) -> part
# select(Category, malebias, femalebias, total, sex, percent, samplesize)
dat_p1 <- bind_rows(dat_p1, part)
p1 <- ggplot(dat_p1) + aes(x = Category, y = percent, fill = sex) + geom_col() +
geom_hline(yintercept = 50, linetype = "dashed", color = "gray40") + geom_text(data = subset(dat_p1,
samplesize != 0), aes(label = samplesize), position = position_stack(vjust = 0.5),
color = "white", size = 3.5) + scale_fill_manual(values = colours) + theme_bw(base_size = 18) +
theme(strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15,
r = 15, b = 15, l = 15)), strip.text.x = element_text(size = 12), strip.background = element_rect(colour = NULL,
linetype = "blank", fill = "gray90"), text = element_text(size = 14), panel.spacing = unit(0.5,
"lines"), panel.border = element_blank(), axis.line = element_line(), panel.grid.major.x = element_line(linetype = "solid",
colour = "gray95"), panel.grid.major.y = element_line(linetype = "solid",
colour = "gray95"), panel.grid.minor.y = element_blank(), panel.grid.minor.x = element_blank(),
axis.title.x = element_blank(), axis.title.y = element_blank(), plot.title = element_text(size = 14),
legend.position = "none") + coord_flip() + labs(title = "Scenario A - different slopes, \n same intercepts")
# sex bias in intercept parameter - scenario B
dat_p2 <- dat_int %>%
group_by_at(vars(Category)) %>%
summarise(malebias = sum(m_intercept > f_intercept), femalebias = sum(f_intercept >
m_intercept), total = malebias + femalebias, malepercent = malebias * 100/total,
femalepercent = femalebias * 100/total)
dat_p2 <- gather(as.data.frame(dat_p2), key = sex, value = percent, malepercent:femalepercent,
factor_key = TRUE)
dat_p2$samplesize <- with(dat_p2, ifelse(sex == "malepercent", malebias, femalebias))
# addeing All
dat_p2 %>%
group_by(sex) %>%
summarise(malebias = sum(malebias), femalebias = sum(femalebias), total = sum(total),
) -> part2
part2 %>%
mutate(Category = "All", sex = c("malepercent", "femalepercent"), percent = c(100 *
(malebias[1]/total[1]), 100 * (femalebias[1]/total[1])), samplesize = c(malebias[1],
femalebias[1])) -> part2
# select(Category, malebias, femalebias, total, sex, percent, samplesize)
dat_p2 <- bind_rows(dat_p2, part2)
p2 <- ggplot(dat_p2) + aes(x = Category, y = percent, fill = sex) + geom_col() +
geom_hline(yintercept = 50, linetype = "dashed", color = "gray40") + geom_text(data = subset(dat_p2,
samplesize != 0), aes(label = samplesize), position = position_stack(vjust = 0.5),
color = "white", size = 3.5) + scale_fill_manual(values = colours) + theme_bw(base_size = 18) +
theme(strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15,
r = 15, b = 15, l = 15)), strip.text.x = element_text(size = 12), strip.background = element_rect(colour = NULL,
linetype = "blank", fill = "gray90"), text = element_text(size = 14), panel.spacing = unit(0.5,
"lines"), panel.border = element_blank(), axis.line = element_line(), panel.grid.major.x = element_line(linetype = "solid",
colour = "gray95"), panel.grid.major.y = element_line(linetype = "solid",
color = "gray95"), panel.grid.minor.y = element_blank(), panel.grid.minor.x = element_blank(),
axis.title.x = element_blank(), axis.title.y = element_blank(), plot.title = element_text(size = 14),
legend.position = "none") + coord_flip() + labs(title = "Scenario B - same slopes, \n different intercepts")
# sex bias in sig intercept and slope parameter - scenario C
dat_p3 <- dat_intSlopes %>%
group_by_at(vars(Category)) %>%
summarise(malebias = sum(m_intercept > f_intercept & m_slope > f_slope), mixed = sum(m_intercept >
f_intercept & m_slope < f_slope, m_intercept < f_intercept & m_slope > f_slope),
femalebias = sum(f_intercept > m_intercept & f_slope > m_slope), total = malebias +
mixed + femalebias, malepercent = malebias * 100/total, mixedpercent = mixed *
100/total, femalepercent = femalebias * 100/total)
dat_p3 <- gather(as.data.frame(dat_p3), key = sex, value = percent, malepercent:femalepercent,
factor_key = TRUE)
dat_p3$samplesize <- with(dat_p3, ifelse(sex == "malepercent", malebias, ifelse(sex ==
"mixedpercent", mixed, femalebias)))
# addeng All
dat_p3 %>%
group_by(sex) %>%
summarise(malebias = sum(malebias), mixed = sum(mixed), femalebias = sum(femalebias),
total = sum(total), ) -> part3
part3 %>%
mutate(Category = "All", sex = c("malepercent", "mixedpercent", "femalepercent"),
percent = c(100 * (malebias[1]/total[1]), 100 * (mixed[1]/total[1]), 100 *
(femalebias[1]/total[1])), samplesize = c(malebias[1], mixed[1], femalebias[1])) ->
part3
# select(Category, malebias, femalebias, total, sex, percent, samplesize)
dat_p3 <- bind_rows(dat_p3, part3)
p3 <- ggplot(dat_p3) + aes(x = Category, y = percent, fill = sex) + geom_col() +
geom_hline(yintercept = 50, linetype = "dashed", color = "gray40") + geom_text(data = subset(dat_p3,
samplesize != 0), aes(label = samplesize), position = position_stack(vjust = 0.5),
color = "white", size = 3.5) + scale_fill_manual(values = colours2) + theme_bw(base_size = 18) +
theme(strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15,
r = 15, b = 15, l = 15)), strip.text.x = element_text(size = 12), strip.background = element_rect(colour = NULL,
linetype = "blank", fill = "gray90"), text = element_text(size = 14), panel.spacing = unit(0.5,
"lines"), panel.border = element_blank(), axis.line = element_line(), panel.grid.major.x = element_line(linetype = "solid",
colour = "gray95"), panel.grid.major.y = element_line(linetype = "solid",
color = "gray95"), panel.grid.minor.y = element_blank(), panel.grid.minor.x = element_blank(),
axis.title.y = element_blank(), plot.title = element_text(size = 14), legend.position = "none") +
ylab("Percentage (%)") + coord_flip() + labs(title = "Scenario C - different slopes, \n different intercepts")
# sex bias in sd
dat_p4 <- dat %>%
filter(p_val_sd <= 0.05) %>%
group_by_at(vars(Category)) %>%
summarise(malebias = sum(m_sd > f_sd), femalebias = sum(f_sd > m_sd), total = malebias +
femalebias, malepercent = malebias * 100/total, femalepercent = femalebias *
100/total)
dat_p4 <- gather(as.data.frame(dat_p4), key = sex, value = percent, malepercent:femalepercent,
factor_key = TRUE)
dat_p4$samplesize <- with(dat_p4, ifelse(sex == "malepercent", malebias, femalebias))
# addeing All
dat_p4 %>%
group_by(sex) %>%
summarise(malebias = sum(malebias), femalebias = sum(femalebias), total = sum(total),
) -> part4
part4 %>%
mutate(Category = "All", sex = c("malepercent", "femalepercent"), percent = c(100 *
(malebias[1]/total[1]), 100 * (femalebias[1]/total[1])), samplesize = c(malebias[1],
femalebias[1])) -> part4
# select(Category, malebias, femalebias, total, sex, percent, samplesize)
dat_p4 <- bind_rows(dat_p4, part4)
p4 <- ggplot(dat_p4) + aes(x = Category, y = percent, fill = sex) + geom_col() +
geom_hline(yintercept = 50, linetype = "dashed", color = "gray40") + geom_text(data = subset(dat_p4,
samplesize != 0), aes(label = samplesize), position = position_stack(vjust = 0.5),
color = "white", size = 3.5) + scale_fill_manual(values = colours) + theme_bw(base_size = 18) +
theme(strip.text.y = element_text(angle = 270, size = 10, margin = margin(t = 15,
r = 15, b = 15, l = 15)), strip.text.x = element_text(size = 12), strip.background = element_rect(colour = NULL,
linetype = "blank", fill = "gray90"), text = element_text(size = 14), panel.spacing = unit(0.5,
"lines"), panel.border = element_blank(), axis.line = element_line(), panel.grid.major.x = element_line(linetype = "solid",
colour = "gray95"), panel.grid.major.y = element_line(linetype = "solid",
color = "gray95"), panel.grid.minor.y = element_blank(), panel.grid.minor.x = element_blank(),
axis.title.y = element_blank(), plot.title = element_text(size = 14), legend.position = "none") +
ylab("Percentage (%)") + coord_flip() + labs(title = "Statistically significant\nsex difference in residual SDs")
# putting all together
(p1 + p2)/(p3 + p4) + plot_annotation(tag_levels = "A")Fig. 2 Sex biases for mice phenotypic traits arrange in functional groups (see the main tex)
Functional categories in the dataset
par(mar = c(6, 6, 6, 6))
v = c(85, 39, 21, 31, 25, 111, 8, 22, 33)
t = c("behaviour", "eye", "hearing", "heart", "hematology", "immunology", "metabolism",
"morphology", "physiology")
d = data.frame(trait = t, n = v)
d = d[order(d$n, decreasing = TRUE), ]
barplot(height = d$n, names.arg = d$trait, las = 3, col = seq_along(d$trait) + 1,
ylim = c(0, 80))Fig. S1 The number of traits in each of 9 functional categories.
Meta-analysis
Calculating absolute effect sizes
Here we convert our effect sizes to absolute values assuming folded normal distributions.
## for folded normal distribution see:
## https://en.wikipedia.org/wiki/Folded_normal_distribution
# folded mean
folded_mu <- function(mean, variance) {
mu <- mean
sigma <- sqrt(variance)
fold_mu <- sigma * sqrt(2/pi) * exp((-mu^2)/(2 * sigma^2)) + mu * (1 - 2 * pnorm(-mu/sigma))
fold_mu
}
# folded variance
folded_v <- function(mean, variance) {
mu <- mean
sigma <- sqrt(variance)
fold_mu <- sigma * sqrt(2/pi) * exp((-mu^2)/(2 * sigma^2)) + mu * (1 - 2 * pnorm(-mu/sigma))
fold_se <- sqrt(mu^2 + sigma^2 - fold_mu^2)
# adding se to make bigger mean
fold_v <- fold_se^2
fold_v
}
dat <- dat %>%
mutate(abs_int = folded_mu(fm_diff_int, fm_diff_int_se^2), abs_slope = folded_mu(fm_diff_slope,
fm_diff_slope_se^2), abs_lnVR = folded_mu(lnVR, VlnVR), V_abs_int = folded_v(fm_diff_int,
fm_diff_int_se^2), V_abs_slope = folded_v(fm_diff_slope, fm_diff_slope_se^2),
V_abs_lnVR = folded_v(lnVR, VlnVR), total_n = f_n + m_n)Comparing sex difference in intercepts
This is a meta-analytic model of sex differences in intercepts (mean
traits). We use the rubust function calculates to see
whether our results form the model is robust (consistent).
modelia <- rma.mv(yi = abs_int, V = V_abs_int, random = list(~1 | Category, ~1 |
parameter_group, ~1 | obs), data = dat)
summary(modelia)##
## Multivariate Meta-Analysis Model (k = 375; method: REML)
##
## logLik Deviance AIC BIC AICc
## 330.5412 -661.0825 -653.0825 -637.3855 -652.9741
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0007 0.0274 9 no Category
## sigma^2.2 0.0042 0.0648 226 no parameter_group
## sigma^2.3 0.0057 0.0756 375 no obs
##
## Test for Heterogeneity:
## Q(df = 374) = 291608.4731, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0911 0.0117 7.8123 <.0001 0.0682 0.1139 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# robust variance estimator
robust(modelia, cluster = dat$parameter_group)##
## Multivariate Meta-Analysis Model (k = 375; method: REML)
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0007 0.0274 9 no Category
## sigma^2.2 0.0042 0.0648 226 no parameter_group
## sigma^2.3 0.0057 0.0756 375 no obs
##
## Test for Heterogeneity:
## Q(df = 374) = 291608.4731, p-val < .0001
##
## Number of estimates: 375
## Number of clusters: 226
## Estimates per cluster: 1-12 (mean: 1.66, median: 1)
##
## Model Results:
##
## estimate se¹ tval¹ df¹ pval¹ ci.lb¹ ci.ub¹
## 0.0911 0.0083 10.9730 225 <.0001 0.0747 0.1074 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## 1) results based on cluster-robust inference (var-cov estimator: CR1,
## approx. t-test and confidence interval, dfs = residual method)
# I2 (heterogeneity)
i2_ml(modelia)## I2_Total I2_Category I2_parameter_group I2_obs
## 99.999777 7.024528 39.381738 53.593510
This is a meta-regression model of sex differences in intercepts
(mean traits) with a functional category as a moderator. We used the
rubust function calculates to see whether our results form
the model is robust (consistent).
model1a <- rma.mv(yi = abs_int, V = V_abs_int, mod = ~Category - 1, random = list(~1 |
parameter_group, ~1 | obs), data = dat)
summary(model1a)##
## Multivariate Meta-Analysis Model (k = 375; method: REML)
##
## logLik Deviance AIC BIC AICc
## 329.2734 -658.5468 -636.5468 -593.6178 -635.8010
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0043 0.0653 226 no parameter_group
## sigma^2.2 0.0057 0.0753 375 no obs
##
## Test for Residual Heterogeneity:
## QE(df = 366) = 260410.1214, p-val < .0001
##
## Test of Moderators (coefficients 1:9):
## QM(df = 9) = 239.3028, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## CategoryBehaviour 0.1082 0.0122 8.9002 <.0001 0.0844 0.1320 ***
## CategoryEye 0.0328 0.0201 1.6348 0.1021 -0.0065 0.0721
## CategoryHearing 0.0748 0.0414 1.8070 0.0708 -0.0063 0.1559 .
## CategoryHeart 0.0784 0.0185 4.2389 <.0001 0.0421 0.1146 ***
## CategoryHematology 0.0871 0.0229 3.8052 0.0001 0.0422 0.1319 ***
## CategoryImmunology 0.0992 0.0155 6.3926 <.0001 0.0688 0.1296 ***
## CategoryMetabolism 0.1662 0.0354 4.6885 <.0001 0.0967 0.2356 ***
## CategoryMorphology 0.0576 0.0222 2.5974 0.0094 0.0141 0.1011 **
## CategoryPhysiology 0.1273 0.0176 7.2186 <.0001 0.0927 0.1618 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
robust(model1a, cluster = dat$parameter_group)##
## Multivariate Meta-Analysis Model (k = 375; method: REML)
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0043 0.0653 226 no parameter_group
## sigma^2.2 0.0057 0.0753 375 no obs
##
## Test for Residual Heterogeneity:
## QE(df = 366) = 260410.1214, p-val < .0001
##
## Number of estimates: 375
## Number of clusters: 226
## Estimates per cluster: 1-12 (mean: 1.66, median: 1)
##
## Test of Moderators (coefficients 1:9):¹
## F(df1 = 9, df2 = 217) = 29.7755, p-val < .0001
##
## Model Results:
##
## estimate se¹ tval¹ df¹ pval¹ ci.lb¹ ci.ub¹
## CategoryBehaviour 0.1082 0.0104 10.3673 217 <.0001 0.0876 0.1288
## CategoryEye 0.0328 0.0100 3.2869 217 0.0012 0.0131 0.0525
## CategoryHearing 0.0748 0.0480 1.5581 217 0.1207 -0.0198 0.1694
## CategoryHeart 0.0784 0.0213 3.6739 217 0.0003 0.0363 0.1204
## CategoryHematology 0.0871 0.0189 4.6172 217 <.0001 0.0499 0.1242
## CategoryImmunology 0.0992 0.0120 8.2932 217 <.0001 0.0756 0.1228
## CategoryMetabolism 0.1662 0.0742 2.2397 217 0.0261 0.0199 0.3124
## CategoryMorphology 0.0576 0.0206 2.8031 217 0.0055 0.0171 0.0982
## CategoryPhysiology 0.1273 0.0229 5.5499 217 <.0001 0.0821 0.1725
##
## CategoryBehaviour ***
## CategoryEye **
## CategoryHearing
## CategoryHeart ***
## CategoryHematology ***
## CategoryImmunology ***
## CategoryMetabolism *
## CategoryMorphology **
## CategoryPhysiology ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## 1) results based on cluster-robust inference (var-cov estimator: CR1,
## approx. t/F-tests and confidence intervals, dfs = residual method)
# R2 (variance explained)
r2_ml(model1a)## R2_marginal R2_conditional
## 0.07200838 0.46991192
Comparing sex difference in slopes
This is a meta-analytic model of sex differences in slopes. We used
the rubust function calculates to see whether our results
form the model is robust (consistent).
modelsa <- rma.mv(yi = abs_slope, V = V_abs_slope, random = list(~1 | Category, ~1 |
parameter_group, ~1 | obs), data = dat)
summary(modelsa) # not sig this means sometimes male is high other times female has steaper slops##
## Multivariate Meta-Analysis Model (k = 375; method: REML)
##
## logLik Deviance AIC BIC AICc
## 94.7310 -189.4620 -181.4620 -165.7649 -181.3536
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0047 0.0686 9 no Category
## sigma^2.2 0.0136 0.1167 226 no parameter_group
## sigma^2.3 0.0000 0.0000 375 no obs
##
## Test for Heterogeneity:
## Q(df = 374) = 3762.4610, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.1427 0.0255 5.5851 <.0001 0.0926 0.1927 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
robust(modelsa, cluster = dat$parameter_group)##
## Multivariate Meta-Analysis Model (k = 375; method: REML)
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0047 0.0686 9 no Category
## sigma^2.2 0.0136 0.1167 226 no parameter_group
## sigma^2.3 0.0000 0.0000 375 no obs
##
## Test for Heterogeneity:
## Q(df = 374) = 3762.4610, p-val < .0001
##
## Number of estimates: 375
## Number of clusters: 226
## Estimates per cluster: 1-12 (mean: 1.66, median: 1)
##
## Model Results:
##
## estimate se¹ tval¹ df¹ pval¹ ci.lb¹ ci.ub¹
## 0.1427 0.0120 11.9325 225 <.0001 0.1191 0.1662 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## 1) results based on cluster-robust inference (var-cov estimator: CR1,
## approx. t-test and confidence interval, dfs = residual method)
# I2 (heterogeneity)
i2_ml(modelia)## I2_Total I2_Category I2_parameter_group I2_obs
## 99.999777 7.024528 39.381738 53.593510
This is a meta-regression model of sex differences in slopes with a
functional category as a moderator. We use the rubust
function calculates to see whether our results form the model is robust
(consistent).
model2a <- rma.mv(yi = abs_slope, V = V_abs_slope, mod = ~Category - 1, random = list(~1 |
parameter_group, ~1 | obs), data = dat)
summary(model2a)##
## Multivariate Meta-Analysis Model (k = 375; method: REML)
##
## logLik Deviance AIC BIC AICc
## 99.5125 -199.0250 -177.0250 -134.0960 -176.2792
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0138 0.1176 226 no parameter_group
## sigma^2.2 0.0000 0.0000 375 no obs
##
## Test for Residual Heterogeneity:
## QE(df = 366) = 2930.7108, p-val < .0001
##
## Test of Moderators (coefficients 1:9):
## QM(df = 9) = 250.6454, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## CategoryBehaviour 0.1699 0.0227 7.4881 <.0001 0.1255 0.2144 ***
## CategoryEye 0.1248 0.0336 3.7145 0.0002 0.0590 0.1907 ***
## CategoryHearing 0.0515 0.0684 0.7535 0.4512 -0.0825 0.1856
## CategoryHeart 0.0902 0.0247 3.6495 0.0003 0.0417 0.1386 ***
## CategoryHematology 0.0723 0.0302 2.3917 0.0168 0.0131 0.1316 *
## CategoryImmunology 0.2575 0.0311 8.2874 <.0001 0.1966 0.3184 ***
## CategoryMetabolism 0.2723 0.0468 5.8207 <.0001 0.1806 0.3640 ***
## CategoryMorphology 0.0683 0.0277 2.4641 0.0137 0.0140 0.1227 *
## CategoryPhysiology 0.1681 0.0232 7.2481 <.0001 0.1227 0.2136 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
robust(model2a, cluster = dat$parameter_group)##
## Multivariate Meta-Analysis Model (k = 375; method: REML)
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0138 0.1176 226 no parameter_group
## sigma^2.2 0.0000 0.0000 375 no obs
##
## Test for Residual Heterogeneity:
## QE(df = 366) = 2930.7108, p-val < .0001
##
## Number of estimates: 375
## Number of clusters: 226
## Estimates per cluster: 1-12 (mean: 1.66, median: 1)
##
## Test of Moderators (coefficients 1:9):¹
## F(df1 = 9, df2 = 217) = 37.7645, p-val < .0001
##
## Model Results:
##
## estimate se¹ tval¹ df¹ pval¹ ci.lb¹ ci.ub¹
## CategoryBehaviour 0.1699 0.0222 7.6457 217 <.0001 0.1261 0.2137 ***
## CategoryEye 0.1248 0.0218 5.7214 217 <.0001 0.0818 0.1678 ***
## CategoryHearing 0.0515 0.0193 2.6637 217 0.0083 0.0134 0.0897 **
## CategoryHeart 0.0902 0.0128 7.0332 217 <.0001 0.0649 0.1154 ***
## CategoryHematology 0.0723 0.0188 3.8565 217 0.0002 0.0354 0.1093 ***
## CategoryImmunology 0.2575 0.0224 11.4862 217 <.0001 0.2133 0.3017 ***
## CategoryMetabolism 0.2723 0.0734 3.7077 217 0.0003 0.1276 0.4171 ***
## CategoryMorphology 0.0683 0.0204 3.3455 217 0.0010 0.0281 0.1086 ***
## CategoryPhysiology 0.1681 0.0372 4.5149 217 <.0001 0.0947 0.2416 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## 1) results based on cluster-robust inference (var-cov estimator: CR1,
## approx. t/F-tests and confidence intervals, dfs = residual method)
# R2 (variance explained)
r2_ml(model2a)## R2_marginal R2_conditional
## 0.2758679 1.0000000
Comparing sex difference in resdiaul SDs
This is a meta-analytic model of sex differences in residual SDs. We
use the rubust function calculates to see whether our
results form the model is robust (consistent).
modelsda <- rma.mv(yi = abs_lnVR, V = V_abs_lnVR, random = list(~1 | Category, ~1 |
parameter_group, ~1 | obs), data = dat)
summary(modelsda)##
## Multivariate Meta-Analysis Model (k = 375; method: REML)
##
## logLik Deviance AIC BIC AICc
## 161.7970 -323.5939 -315.5939 -299.8969 -315.4855
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0043 0.0660 9 no Category
## sigma^2.2 0.0137 0.1172 226 no parameter_group
## sigma^2.3 0.0105 0.1026 375 no obs
##
## Test for Heterogeneity:
## Q(df = 374) = 17747.7965, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.1604 0.0252 6.3627 <.0001 0.1110 0.2098 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
robust(modelsda, cluster = dat$parameter_group)##
## Multivariate Meta-Analysis Model (k = 375; method: REML)
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0043 0.0660 9 no Category
## sigma^2.2 0.0137 0.1172 226 no parameter_group
## sigma^2.3 0.0105 0.1026 375 no obs
##
## Test for Heterogeneity:
## Q(df = 374) = 17747.7965, p-val < .0001
##
## Number of estimates: 375
## Number of clusters: 226
## Estimates per cluster: 1-12 (mean: 1.66, median: 1)
##
## Model Results:
##
## estimate se¹ tval¹ df¹ pval¹ ci.lb¹ ci.ub¹
## 0.1604 0.0136 11.7707 225 <.0001 0.1336 0.1873 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## 1) results based on cluster-robust inference (var-cov estimator: CR1,
## approx. t-test and confidence interval, dfs = residual method)
# I2 (heterogeneity)
i2_ml(modelsda)## I2_Total I2_Category I2_parameter_group I2_obs
## 98.95330 15.04095 47.53607 36.37628
This is a meta-regression model of sex differences in residual SDs
with a functional category as a moderator. We use the
rubust function calculates to see whether our results form
the model is robust (consistent).
# meta-regression
model3a <- rma.mv(yi = abs_lnVR, V = V_abs_lnVR, mod = ~Category - 1, random = list(~1 |
parameter_group, ~1 | obs), data = dat)
summary(model3a)##
## Multivariate Meta-Analysis Model (k = 375; method: REML)
##
## logLik Deviance AIC BIC AICc
## 166.1813 -332.3626 -310.3626 -267.4336 -309.6168
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0141 0.1186 226 no parameter_group
## sigma^2.2 0.0105 0.1023 375 no obs
##
## Test for Residual Heterogeneity:
## QE(df = 366) = 15145.0610, p-val < .0001
##
## Test of Moderators (coefficients 1:9):
## QM(df = 9) = 272.2427, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## CategoryBehaviour 0.1125 0.0188 5.9862 <.0001 0.0757 0.1494 ***
## CategoryEye 0.3053 0.0357 8.5607 <.0001 0.2354 0.3752 ***
## CategoryHearing 0.0882 0.0724 1.2186 0.2230 -0.0536 0.2300
## CategoryHeart 0.0922 0.0291 3.1648 0.0016 0.0351 0.1492 **
## CategoryHematology 0.1659 0.0370 4.4824 <.0001 0.0934 0.2384 ***
## CategoryImmunology 0.2425 0.0262 9.2487 <.0001 0.1911 0.2939 ***
## CategoryMetabolism 0.1177 0.0557 2.1133 0.0346 0.0085 0.2269 *
## CategoryMorphology 0.1767 0.0353 5.0022 <.0001 0.1074 0.2459 ***
## CategoryPhysiology 0.1132 0.0279 4.0623 <.0001 0.0586 0.1679 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# R2 (variance explained)
r2_ml(model3a)## R2_marginal R2_conditional
## 0.1807622 0.6503808
Comparing model fits
This is a meta-analytic model of Zr (transformed model fits). We use
the rubust function calculates to see whether our results
form the model is robust (consistent).
modelr0 <- rma.mv(yi = Zr, V = VZr, random = list(~1 | Category, ~1 | parameter_group,
~1 | obs), data = dat)
summary(modelr0)##
## Multivariate Meta-Analysis Model (k = 375; method: REML)
##
## logLik Deviance AIC BIC AICc
## 106.1418 -212.2836 -204.2836 -188.5866 -204.1752
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0016 0.0404 9 no Category
## sigma^2.2 0.0151 0.1228 226 no parameter_group
## sigma^2.3 0.0193 0.1390 375 no obs
##
## Test for Heterogeneity:
## Q(df = 374) = 62294.2740, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.1568 0.0188 8.3228 <.0001 0.1199 0.1938 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
robust(modelr0, cluster = dat$parameter_group)##
## Multivariate Meta-Analysis Model (k = 375; method: REML)
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0016 0.0404 9 no Category
## sigma^2.2 0.0151 0.1228 226 no parameter_group
## sigma^2.3 0.0193 0.1390 375 no obs
##
## Test for Heterogeneity:
## Q(df = 374) = 62294.2740, p-val < .0001
##
## Number of estimates: 375
## Number of clusters: 226
## Estimates per cluster: 1-12 (mean: 1.66, median: 1)
##
## Model Results:
##
## estimate se¹ tval¹ df¹ pval¹ ci.lb¹ ci.ub¹
## 0.1568 0.0140 11.2340 225 <.0001 0.1293 0.1843 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## 1) results based on cluster-robust inference (var-cov estimator: CR1,
## approx. t-test and confidence interval, dfs = residual method)
# funnel(modelr0)
i2_ml(modelr0)## I2_Total I2_Category I2_parameter_group I2_obs
## 99.505987 4.508093 41.637698 53.360197
This is a meta-regression model of Zr (transformed model fits). with
a functional category as a moderator. We use the rubust
function calculates to see whether our results form the model is robust
(consistent).
# meta-regression
modelr1 <- rma.mv(yi = Zr, mod = ~Category - 1, V = VZr, random = list(~1 | parameter_group,
~1 | obs), data = dat)
summary(modelr1)##
## Multivariate Meta-Analysis Model (k = 375; method: REML)
##
## logLik Deviance AIC BIC AICc
## 108.6253 -217.2506 -195.2506 -152.3216 -194.5049
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0147 0.1212 226 no parameter_group
## sigma^2.2 0.0196 0.1398 375 no obs
##
## Test for Residual Heterogeneity:
## QE(df = 366) = 52692.9117, p-val < .0001
##
## Test of Moderators (coefficients 1:9):
## QM(df = 9) = 196.4714, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## CategoryBehaviour 0.1560 0.0216 7.2260 <.0001 0.1137 0.1983 ***
## CategoryEye 0.1797 0.0384 4.6842 <.0001 0.1045 0.2549 ***
## CategoryHearing 0.0399 0.0768 0.5192 0.6037 -0.1107 0.1904
## CategoryHeart 0.1638 0.0341 4.8021 <.0001 0.0969 0.2307 ***
## CategoryHematology 0.1140 0.0425 2.6863 0.0072 0.0308 0.1972 **
## CategoryImmunology 0.0784 0.0286 2.7450 0.0061 0.0224 0.1344 **
## CategoryMetabolism 0.1940 0.0656 2.9592 0.0031 0.0655 0.3225 **
## CategoryMorphology 0.2288 0.0412 5.5481 <.0001 0.1479 0.3096 ***
## CategoryPhysiology 0.2183 0.0326 6.6854 <.0001 0.1543 0.2822 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# R2 (variance explained)
r2_ml(modelr1)## R2_marginal R2_conditional
## 0.08210012 0.47577883
Obtaining correlations among intercepts, slopes and SDs
# quad-variate model
mod_lnsd <- bf(log(abs_lnVR) | se(sqrt(V_abs_lnVR)/abs_lnVR) ~ -1 + Category + (1 |
q | parameter_group))
mod_lnslp <- bf(log(abs_slope) | se(sqrt(V_abs_slope)/abs_slope) ~ -1 + Category +
(1 | q | parameter_group))
mod_lnint <- bf(log(abs_int) | se(sqrt(V_abs_int)/abs_int) ~ -1 + Category + (1 |
q | parameter_group))
mod_lnzr <- bf(log(Zr) | se(sqrt(VZr)/Zr) ~ -1 + Category + (1 | q | parameter_group))
fit_4b <- brm(mod_lnsd + mod_lnslp + mod_lnint + mod_lnzr, data = dat, chains = 2,
cores = 2, iter = 4000, warmup = 1000, backend = "cmdstanr")
summary(fit_4b)
# saving the model saveRDS(fit_3b, file = here('data', 'fit_3b.rds'))## Family: MV(gaussian, gaussian, gaussian, gaussian)
## Links: mu = identity; sigma = identity
## mu = identity; sigma = identity
## mu = identity; sigma = identity
## mu = identity; sigma = identity
## Formula: log(abs_lnVR) | se(sqrt(V_abs_lnVR)/abs_lnVR) ~ -1 + Category + (1 | q | parameter_group)
## log(abs_slope) | se(sqrt(V_abs_slope)/abs_slope) ~ -1 + Category + (1 | q | parameter_group)
## log(abs_int) | se(sqrt(V_abs_int)/abs_int) ~ -1 + Category + (1 | q | parameter_group)
## log(Zr) | se(sqrt(VZr)/Zr) ~ -1 + Category + (1 | q | parameter_group)
## Data: dat (Number of observations: 375)
## Draws: 2 chains, each with iter = 3000; warmup = 0; thin = 1;
## total post-warmup draws = 6000
##
## Group-Level Effects:
## ~parameter_group (Number of levels: 226)
## Estimate Est.Error l-95% CI
## sd(logabslnVR_Intercept) 0.82 0.05 0.74
## sd(logabsslope_Intercept) 1.25 0.07 1.12
## sd(logabsint_Intercept) 1.44 0.07 1.30
## sd(logZr_Intercept) 0.90 0.05 0.81
## cor(logabslnVR_Intercept,logabsslope_Intercept) 0.09 0.07 -0.05
## cor(logabslnVR_Intercept,logabsint_Intercept) 0.04 0.07 -0.10
## cor(logabsslope_Intercept,logabsint_Intercept) 0.74 0.04 0.67
## cor(logabslnVR_Intercept,logZr_Intercept) 0.16 0.07 0.02
## cor(logabsslope_Intercept,logZr_Intercept) 0.39 0.07 0.26
## cor(logabsint_Intercept,logZr_Intercept) 0.70 0.04 0.62
## u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(logabslnVR_Intercept) 0.92 1.00 1575 2856
## sd(logabsslope_Intercept) 1.40 1.00 1996 3191
## sd(logabsint_Intercept) 1.59 1.00 1446 2373
## sd(logZr_Intercept) 1.00 1.00 1462 2409
## cor(logabslnVR_Intercept,logabsslope_Intercept) 0.24 1.00 1040 2492
## cor(logabslnVR_Intercept,logabsint_Intercept) 0.17 1.00 1118 2019
## cor(logabsslope_Intercept,logabsint_Intercept) 0.81 1.00 1368 2723
## cor(logabslnVR_Intercept,logZr_Intercept) 0.30 1.00 1096 2123
## cor(logabsslope_Intercept,logZr_Intercept) 0.51 1.00 1032 2216
## cor(logabsint_Intercept,logZr_Intercept) 0.77 1.00 1541 3338
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat
## logabslnVR_CategoryBehaviour -2.29 0.11 -2.51 -2.07 1.00
## logabslnVR_CategoryEye -1.30 0.21 -1.70 -0.86 1.00
## logabslnVR_CategoryHearing -2.04 0.47 -2.99 -1.12 1.00
## logabslnVR_CategoryHeart -2.60 0.16 -2.92 -2.29 1.00
## logabslnVR_CategoryHematology -2.02 0.22 -2.44 -1.59 1.00
## logabslnVR_CategoryImmunology -1.46 0.16 -1.78 -1.16 1.00
## logabslnVR_CategoryMetabolism -2.69 0.32 -3.32 -2.10 1.00
## logabslnVR_CategoryMorphology -2.19 0.20 -2.58 -1.81 1.00
## logabslnVR_CategoryPhysiology -2.48 0.16 -2.78 -2.17 1.01
## logabsslope_CategoryBehaviour -0.99 0.17 -1.33 -0.67 1.00
## logabsslope_CategoryEye -1.89 0.31 -2.51 -1.28 1.00
## logabsslope_CategoryHearing -2.95 0.75 -4.42 -1.49 1.00
## logabsslope_CategoryHeart -2.38 0.25 -2.88 -1.90 1.00
## logabsslope_CategoryHematology -2.87 0.32 -3.49 -2.23 1.00
## logabsslope_CategoryImmunology -0.99 0.25 -1.48 -0.51 1.00
## logabsslope_CategoryMetabolism -1.49 0.46 -2.42 -0.58 1.00
## logabsslope_CategoryMorphology -3.98 0.30 -4.58 -3.39 1.00
## logabsslope_CategoryPhysiology -2.19 0.23 -2.66 -1.73 1.00
## logabsint_CategoryBehaviour -2.37 0.18 -2.72 -2.02 1.00
## logabsint_CategoryEye -3.85 0.35 -4.55 -3.14 1.00
## logabsint_CategoryHearing -3.20 0.83 -4.81 -1.54 1.00
## logabsint_CategoryHeart -3.26 0.27 -3.80 -2.72 1.00
## logabsint_CategoryHematology -3.06 0.35 -3.75 -2.38 1.00
## logabsint_CategoryImmunology -2.33 0.27 -2.86 -1.79 1.00
## logabsint_CategoryMetabolism -2.68 0.53 -3.74 -1.68 1.00
## logabsint_CategoryMorphology -5.04 0.34 -5.69 -4.37 1.00
## logabsint_CategoryPhysiology -2.71 0.25 -3.21 -2.22 1.00
## logZr_CategoryBehaviour -2.07 0.11 -2.30 -1.85 1.00
## logZr_CategoryEye -1.75 0.23 -2.19 -1.30 1.00
## logZr_CategoryHearing -2.90 0.53 -3.92 -1.87 1.00
## logZr_CategoryHeart -2.44 0.18 -2.79 -2.08 1.00
## logZr_CategoryHematology -2.30 0.23 -2.75 -1.86 1.00
## logZr_CategoryImmunology -2.42 0.19 -2.81 -2.05 1.00
## logZr_CategoryMetabolism -1.92 0.34 -2.60 -1.28 1.00
## logZr_CategoryMorphology -2.37 0.23 -2.83 -1.92 1.00
## logZr_CategoryPhysiology -1.84 0.16 -2.15 -1.53 1.00
## Bulk_ESS Tail_ESS
## logabslnVR_CategoryBehaviour 1016 2040
## logabslnVR_CategoryEye 1498 2319
## logabslnVR_CategoryHearing 2948 3541
## logabslnVR_CategoryHeart 1448 2488
## logabslnVR_CategoryHematology 920 1872
## logabslnVR_CategoryImmunology 698 1588
## logabslnVR_CategoryMetabolism 2418 3272
## logabslnVR_CategoryMorphology 1112 1953
## logabslnVR_CategoryPhysiology 994 1775
## logabsslope_CategoryBehaviour 1536 2727
## logabsslope_CategoryEye 1394 2667
## logabsslope_CategoryHearing 3230 3975
## logabsslope_CategoryHeart 1653 2584
## logabsslope_CategoryHematology 1287 2068
## logabsslope_CategoryImmunology 914 1686
## logabsslope_CategoryMetabolism 1474 2909
## logabsslope_CategoryMorphology 1430 2414
## logabsslope_CategoryPhysiology 981 1647
## logabsint_CategoryBehaviour 1523 2460
## logabsint_CategoryEye 1497 2693
## logabsint_CategoryHearing 3015 3766
## logabsint_CategoryHeart 1615 2770
## logabsint_CategoryHematology 1181 1985
## logabsint_CategoryImmunology 1085 1592
## logabsint_CategoryMetabolism 1618 2862
## logabsint_CategoryMorphology 1513 2540
## logabsint_CategoryPhysiology 992 1753
## logZr_CategoryBehaviour 1361 2536
## logZr_CategoryEye 2215 3391
## logZr_CategoryHearing 3730 4296
## logZr_CategoryHeart 1710 2837
## logZr_CategoryHematology 1099 2299
## logZr_CategoryImmunology 1293 2367
## logZr_CategoryMetabolism 1873 2830
## logZr_CategoryMorphology 1356 2568
## logZr_CategoryPhysiology 907 1908
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma_logabslnVR 0.00 0.00 0.00 0.00 NA NA NA
## sigma_logabsslope 0.00 0.00 0.00 0.00 NA NA NA
## sigma_logabsint 0.00 0.00 0.00 0.00 NA NA NA
## sigma_logZr 0.00 0.00 0.00 0.00 NA NA NA
##
## Residual Correlations:
## Estimate Est.Error l-95% CI u-95% CI Rhat
## rescor(logabslnVR,logabsslope) 0.07 0.03 0.00 0.14 1.00
## rescor(logabslnVR,logabsint) -0.02 0.01 -0.04 0.01 1.00
## rescor(logabsslope,logabsint) 0.07 0.01 0.04 0.10 1.00
## rescor(logabslnVR,logZr) 0.02 0.01 -0.01 0.04 1.00
## rescor(logabsslope,logZr) -0.01 0.01 -0.04 0.02 1.00
## rescor(logabsint,logZr) 0.46 0.01 0.44 0.48 1.00
## Bulk_ESS Tail_ESS
## rescor(logabslnVR,logabsslope) 10322 4856
## rescor(logabslnVR,logabsint) 15292 4252
## rescor(logabsslope,logabsint) 12868 4787
## rescor(logabslnVR,logZr) 16524 4180
## rescor(logabsslope,logZr) 14510 4609
## rescor(logabsint,logZr) 14145 4041
##
## Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
Creating Figure 3
# colour-blind freindly colour
cbpl <- c("#E69F00", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7", "#56B4E9",
"#AA4499", "#DDCC77")
point.size = 2
branch.size = 3.5
# meta-analysis with model fit
t1 <- orchard_plot2(modelr0, mod = "Int", xlab = "Zr (transformed variance accounted for)",
angle = 45, point.size = point.size, branch.size = branch.size, k = F, N = dat$total_n) +
scale_y_discrete(labels = "") + scale_fill_manual(values = "#999999") + scale_colour_manual(values = "#999999") #+
# xlim(c(-0.5, 1.5))
# meta-regression with model fit
t2 <- orchard_plot2(modelr1, mod = "Category", xlab = "Zr (transformed variance accounted for)",
angle = 45, point.size = point.size, k = F, N = dat$total_n, branch.size = branch.size,
) + scale_y_discrete(labels = rep("", 9)) + scale_fill_manual(values = cbpl) +
scale_colour_manual(values = cbpl) #+
# xlim(c(-0.5, 1.5))
# intercept meta-analysis
p1 <- orchard_plot2(modelia, mod = "Int", xlab = "Absolute difference in standardized intercepts (F-M)",
angle = 45, point.size = point.size, N = dat$total_n, legend.on = FALSE, branch.size = branch.size,
) + scale_y_discrete(labels = "Overall") + scale_fill_manual(values = "#999999") +
scale_colour_manual(values = "#999999") #+
# xlim(c(-0.5, 1.5))
# intercept meta-regression
p2 <- orchard_plot2(model1a, mod = "Category", xlab = "Absolute difference in standardized intercepts (F-M)",
angle = 45, point.size = point.size, N = dat$total_n, legend.on = FALSE, branch.size = branch.size,
) + scale_fill_manual(values = cbpl) + scale_colour_manual(values = cbpl) #+
# xlim(c(-0.5, 1.5))
# slope meta-analysis
p3 <- orchard_plot2(modelsa, mod = "Int", xlab = "Absolute difference in standardized slopes (F-M)",
angle = 45, point.size = point.size, k = F, N = dat$total_n, legend.on = FALSE,
branch.size = branch.size, ) + scale_y_discrete(labels = "") + scale_fill_manual(values = "#999999") +
scale_colour_manual(values = "#999999") #+
# xlim(c(-1.5, 10))
# slope meta-regression
p4 <- orchard_plot2(model2a, mod = "Category", xlab = "Absolute difference in standardized slopes (F-M)",
angle = 45, cb = F, point.size = point.size, k = F, N = dat$total_n, legend.on = FALSE,
branch.size = branch.size, ) + scale_y_discrete(labels = rep("", 9)) + scale_fill_manual(values = cbpl) +
scale_colour_manual(values = cbpl) #+
# xlim(c(-1.5, 10))
# SD meta-analysis
p5 <- orchard_plot2(modelsda, mod = "Category", xlab = "Absolute relative difference in SD (lnVR: F/M)",
angle = 45, point.size = point.size, k = F, N = dat$total_n, legend.on = FALSE,
branch.size = branch.size, ) + scale_y_discrete(labels = "") + scale_fill_manual(values = "#999999") +
scale_colour_manual(values = "#999999") #+
# xlim(c(-0.2, 1.9))
# SD meta-regression
p6 <- orchard_plot2(model3a, mod = "Category", xlab = "Absolute relative difference in SD (lnVR: F/M)",
angle = 45, cb = F, point.size = point.size, k = F, N = dat$total_n, legend.on = FALSE,
branch.size = branch.size, ) + scale_y_discrete(labels = rep("", 9)) + scale_fill_manual(values = cbpl) +
scale_colour_manual(values = cbpl) #+
# xlim(c(-0.2, 1.9))
# putting it together
(p1 | p3 | p5 | t1)/(p2 | p4 | p6 | t2) + plot_layout(heights = c(1, 3)) + plot_annotation(tag_levels = "A")Fig. 3 Orchard plots illustrating results of multilevel meta-analyses (see the main text)
Creating Figure 4
# creating added precision
# dat %>% mutate(pre_slp_int = 1/sqrt(V_abs_int/abs_int^2 +
# V_abs_slope/abs_slope^2), pre_slp_sd = 1/sqrt(V_abs_slope/abs_slope^2 +
# V_abs_lnVR/abs_lnVR^2), pre_int_sd = 1/sqrt(V_abs_int/abs_int^2 +
# V_abs_lnVR/abs_lnVR^2) ) -> dat
# colour-blind freindly colour
cbpl <- c("#E69F00", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7", "#56B4E9",
"#AA4499", "#DDCC77")
f1 <- ggplot(data = dat) + geom_point(aes(x = log(abs_slope), y = log(abs_int), col = Category,
size = total_n)) + scale_fill_manual(values = cbpl) + scale_colour_manual(values = cbpl) +
labs(x = "ln(Absolute difference in standardized slopes)", y = "ln(Absolute difference in standardized intercepts)") +
labs(color = "Trait types", size = "Sample size (N)") + annotate(geom = "text",
x = -7.8, y = -1, label = "r = 0.74 [0.67, 0.81]", size = 3) + theme_bw() + theme(legend.key.size = unit(0.5,
"cm"), legend.title = element_text(size = 10)) + guides(col = "none", size = "none")
f2 <- ggplot(data = dat) + geom_point(aes(x = log(abs_slope), y = log(abs_lnVR),
col = Category, size = total_n)) + scale_fill_manual(values = cbpl) + scale_colour_manual(values = cbpl) +
labs(x = "ln(Absolute difference in standardized slopes)", y = "ln(Absolute relative difference in SD)") +
labs(color = "Trait types", size = "Sample size (N)") + annotate(geom = "text",
x = -7.5, y = 0.5, label = "r = 0.09 [-0.05., 0.24]", size = 3) + theme_bw() +
theme(legend.key.size = unit(0.5, "cm"), legend.title = element_text(size = 10)) +
guides(size = "none", col = "none")
# scale_size_continuous(breaks = c(200, 2000, 20000), guide = guide_legend()) +
f3 <- ggplot(data = dat) + geom_point(aes(x = log(abs_int), y = log(abs_lnVR), col = Category,
size = total_n)) + scale_fill_manual(values = cbpl) + scale_colour_manual(values = cbpl) +
labs(x = "ln(Absolute difference in standardized intercepts)", y = "ln(Absolute relative difference in SD)") +
labs(color = "Trait types", size = "Sample size (N)") + annotate(geom = "text",
x = -10, y = 0.5, label = "r = 0.04 [-0.10, 0.17]", size = 3) + theme_bw() +
theme(legend.key.size = unit(0.5, "cm"), legend.title = element_text(size = 10)) +
guides(size = "none", col = "none")
f4 <- ggplot(data = dat) + geom_point(aes(y = log(Zr), x = log(abs_int), col = Category,
size = total_n)) + scale_fill_manual(values = cbpl) + scale_colour_manual(values = cbpl) +
labs(y = "Zr (transformed variance accounted for)", x = "ln(Absolute difference in standardized intercepts)") +
labs(color = "Trait types", size = "Sample size (N)") + annotate(geom = "text",
x = -2.5, y = -6, label = "r = 0.70 [0.62., 0.77]", size = 3) + theme_bw() +
theme(legend.key.size = unit(0.5, "cm"), legend.title = element_text(size = 10)) +
guides(size = "none") + theme(legend.position = c(0.03, 0.97), legend.justification = c(0,
0.97))
f5 <- ggplot(data = dat) + geom_point(aes(y = log(Zr), x = log(abs_slope), col = Category,
size = total_n)) + scale_fill_manual(values = cbpl) + scale_colour_manual(values = cbpl) +
labs(y = "Zr (transformed variance accounted for)", x = "ln(Absolute difference in standardized slopes)") +
labs(color = "Trait types", size = "Sample size (N)") + annotate(geom = "text",
x = 0, y = -6, label = "r = 0.39 [0.26, 0.51]", size = 3) + theme_bw() + theme(legend.key.size = unit(0.5,
"cm"), legend.title = element_text(size = 10)) + guides(col = "none") + scale_size_continuous(breaks = c(200,
2000, 20000), guide = guide_legend()) + theme(legend.position = c(0.03, 0.97),
legend.justification = c(0, 0.97))
f6 <- ggplot(data = dat) + geom_point(aes(y = log(Zr), x = log(abs_lnVR), col = Category,
size = total_n)) + scale_fill_manual(values = cbpl) + scale_colour_manual(values = cbpl) +
labs(y = "Zr (transformed variance accounted for)", x = "ln(Absolute relative difference in SD)") +
labs(color = "Trait types", size = "Sample size (N)") + annotate(geom = "text",
x = -0.25, y = -6, label = "r = 0.16 [0.02, 0.30]", size = 3) + theme_bw() +
theme(legend.key.size = unit(0.5, "cm"), legend.title = element_text(size = 10)) +
guides(col = "none", size = "none") #+
# scale_size_continuous(breaks = c(200, 2000, 20000), guide = guide_legend())
(f3 | f2)/(f1 | f4)/(f5 | f6) + plot_annotation(tag_levels = "A")Fig. 4 Bivariate ordinations of log absolute difference between males and females (see the main text)
Software and package versions
sessionInfo() %>%
pander()R version 4.1.3 (2022-03-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
locale: en_AU.UTF-8||en_AU.UTF-8||en_AU.UTF-8||C||en_AU.UTF-8||en_AU.UTF-8
attached base packages: stats, graphics, grDevices, utils, datasets, methods and base
other attached packages: rstan(v.2.21.3), StanHeaders(v.2.21.0-7), knitr(v.1.39), formatR(v.1.11), kableExtra(v.1.3.4), brms(v.2.16.3), Rcpp(v.1.0.8.3), pander(v.0.6.5), nlme(v.3.1-155), here(v.1.0.1), broom.mixed(v.0.2.7), orchaRd(v.2.0), patchwork(v.1.1.1), poolr(v.1.1-1), metafor(v.3.4-0), metadat(v.1.2-0), Matrix(v.1.4-0), forcats(v.0.5.1), stringr(v.1.4.0), dplyr(v.1.0.9), purrr(v.0.3.4), readr(v.2.1.2), tidyr(v.1.2.0), tibble(v.3.1.7), ggplot2(v.3.3.6) and tidyverse(v.1.3.1)
loaded via a namespace (and not attached): readxl(v.1.4.0), backports(v.1.4.1), systemfonts(v.1.0.4), plyr(v.1.8.6), igraph(v.1.2.11), splines(v.4.1.3), crosstalk(v.1.2.0), TH.data(v.1.1-0), rstantools(v.2.1.1), inline(v.0.3.19), digest(v.0.6.29), htmltools(v.0.5.2), fansi(v.1.0.3), magrittr(v.2.0.3), checkmate(v.2.0.0), tzdb(v.0.3.0), modelr(v.0.1.8), RcppParallel(v.5.1.5), matrixStats(v.0.61.0), svglite(v.2.1.0), xts(v.0.12.1), sandwich(v.3.0-1), rmdformats(v.1.0.3), prettyunits(v.1.1.1), colorspace(v.2.0-3), rvest(v.1.0.2), haven(v.2.5.0), xfun(v.0.31), callr(v.3.7.0), crayon(v.1.5.1), jsonlite(v.1.8.0), survival(v.3.2-13), zoo(v.1.8-9), glue(v.1.6.2), gtable(v.0.3.0), emmeans(v.1.7.4-1), webshot(v.0.5.2), distributional(v.0.3.0), pkgbuild(v.1.3.1), abind(v.1.4-5), scales(v.1.2.0), mvtnorm(v.1.1-3), DBI(v.1.1.2), miniUI(v.0.1.1.1), viridisLite(v.0.4.0), xtable(v.1.8-4), stats4(v.4.1.3), DT(v.0.21), htmlwidgets(v.1.5.4), httr(v.1.4.3), threejs(v.0.3.3), posterior(v.1.2.1), ellipsis(v.0.3.2), pkgconfig(v.2.0.3), loo(v.2.5.0), farver(v.2.1.0), sass(v.0.4.1), dbplyr(v.2.1.1), utf8(v.1.2.2), labeling(v.0.4.2), tidyselect(v.1.1.2), rlang(v.1.0.2), reshape2(v.1.4.4), later(v.1.3.0), munsell(v.0.5.0), cellranger(v.1.1.0), tools(v.4.1.3), cli(v.3.3.0), generics(v.0.1.2), broom(v.0.8.0), mathjaxr(v.1.6-0), ggridges(v.0.5.3), evaluate(v.0.15), fastmap(v.1.1.0), yaml(v.2.3.5), processx(v.3.5.3), fs(v.1.5.2), mime(v.0.12), xml2(v.1.3.3), compiler(v.4.1.3), bayesplot(v.1.9.0), shinythemes(v.1.2.0), rstudioapi(v.0.13), reprex(v.2.0.1), bslib(v.0.3.1), stringi(v.1.7.6), highr(v.0.9), ps(v.1.7.0), Brobdingnag(v.1.2-7), lattice(v.0.20-45), markdown(v.1.1), shinyjs(v.2.1.0), tensorA(v.0.36.2), vctrs(v.0.4.1), pillar(v.1.7.0), lifecycle(v.1.0.1), jquerylib(v.0.1.4), bridgesampling(v.1.1-2), estimability(v.1.3), httpuv(v.1.6.5), R6(v.2.5.1), bookdown(v.0.25), promises(v.1.2.0.1), gridExtra(v.2.3), codetools(v.0.2-18), MASS(v.7.3-55), colourpicker(v.1.1.1), gtools(v.3.9.2), assertthat(v.0.2.1), rprojroot(v.2.0.2), withr(v.2.5.0), shinystan(v.2.6.0), multcomp(v.1.4-18), parallel(v.4.1.3), hms(v.1.1.1), grid(v.4.1.3), coda(v.0.19-4), rmarkdown(v.2.14), shiny(v.1.7.1), lubridate(v.1.8.0), base64enc(v.0.1-3) and dygraphs(v.1.1.1.6)